human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "inter operator resource management for millimeter wave multi hop backhaul networks"
  Abstract: In this paper, a novel framework is proposed for optimizing the operation and performance of a large-scale, multi-hop millimeter wave (mmW) backhaul within a wireless small cell network (SCN) that encompasses multiple mobile network operators (MNOs). The proposed framework enables the small base stations (SBSs) to jointly decide on forming the multi-hop, mmW links over backhaul infrastructure that belongs to multiple, independent MNOs, while properly allocating resources across those links. In this regard, the problem is addressed using a novel framework based on matching theory that is composed to two, highly inter-related stages: a multi-hop network formation stage and a resource management stage. One unique feature of this framework is that it jointly accounts for both wireless channel characteristics and economic factors during both network formation and resource management. The multi-hop network formation stage is formulated as a one-to-many matching game which is solved using a novel algorithm, that builds on the so-called deferred acceptance algorithm and is shown to yield a stable and Pareto optimal multi-hop mmW backhaul network. Then, a one-to-many matching game is formulated to enable proper resource allocation across the formed multi-hop network. This game is then shown to exhibit peer effects and, as such, a novel algorithm is developed to find a stable and optimal resource management solution that can properly cope with these peer effects. Simulation results show that the proposed framework yields substantial gains, in terms of the average sum rate, reaching up to 27% and 54%, respectively, compared to a non-cooperative scheme in which inter-operator sharing is not allowed and a random allocation approach. The results also show that our framework provides insights on how to manage pricing and the cost of the cooperative mmW backhaul network for the MNOs.

Connected through hyperedge HE-1 (induced by source paper "max min fair resource allocation in millimetre wave backhauls"):
  with members:
    - "adaptive beamwidth selection for contention based access periods in millimeter wave wlans"
    - "maximising the utility of enterprise millimetre wave networks"
    - "position aided beam alignment for millimeter wave backhaul systems with large phased arrays"
    - "long term mobile traffic forecasting using deep spatio temporal neural networks"
    - "dynamic time domain duplexing for self backhauled millimeter wave cellular networks"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"
    - "spatially sparse precoding in millimeter wave mimo systems"
    - "millimeter wave beamforming for wireless backhaul and access in small cell networks"

Connected through hyperedge HE-2 (induced by source paper "federated echo state learning for minimizing breaks in presence in wireless virtual reality networks"):
  with members:
    - "distributed federated learning for ultra reliable low latency vehicular communications"
    - "computation scheduling for distributed machine learning with straggling workers"
    - "towards federated learning at scale system design"
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "communication computing and caching for mobile vr delivery modeling and trade off"
    - "learning to communicate in uav aided wireless networks map based approaches"
    - "edge computing meets millimeter wave enabled vr paving the way to cutting the cord"
    - "analysis of memory capacity for deep echo state networks"

Connected through hyperedge HE-3 (induced by source paper "max min rates in self backhauled millimeter wave cellular networks"):
  with members:
    - "improving the coverage and spectral efficiency of millimeter wave cellular networks using device to device relays"
    - "modeling and analyzing millimeter wave cellular systems"
    - "performance of dynamic and static tdd in self backhauled millimeter wave cellular networks"
    - "optimal joint routing and scheduling in millimeter wave cellular networks"
    - "correction factor for analysis of mimo wireless networks with highly directional beamforming"
    - "gbps user rates using mmwave relayed backhaul with high gain antennas"
    - "mmwave vehicle to infrastructure communication analysis of urban microcellular networks"
    - "tractable model for rate in self backhauled millimeter wave cellular networks"

Connected through hyperedge HE-4 (induced by source paper "the 5g cellular backhaul management dilemma to cache or to serve"):
  with members:
    - "on the tradeoff between energy harvesting and caching in wireless networks"
    - "optimal dynamic multicast scheduling for cache enabled content centric wireless networks"
    - "optimal geographic caching in cellular networks"
    - "caching at the edge a green perspective for 5g networks"
    - "matching theory for backhaul management in small cell networks with mmwave capabilities"
    - "multicast aware caching for small cell networks"
    - "what will 5g be"
    - "living on the edge the role of proactive caching in 5g wireless networks"

Connected through hyperedge HE-5 (induced by source paper "proactive resource management for lte in unlicensed spectrum a deep learning perspective"):
  with members:
    - "deep learning for reliable mobile edge analytics in intelligent transportation systems"
    - "echo state networks for self organizing resource allocation in lte u with uplink downlink decoupling"
    - "lstm a search space odyssey"
    - "deep learning based dynamic watermarking for secure signal authentication in the internet of things"
    - "holistic small cell traffic balancing across licensed and unlicensed bands"
    - "coordinated dynamic spectrum management of lte u and wi fi networks"
    - "learning to cooperate via policy search"
    - "long short term memory based recurrent neural network architectures for large vocabulary speech recognition"

Connected through hyperedge HE-6 (induced by source paper "proactive resource management in lte u systems a deep learning perspective"):
  with members:
    - "deep learning for reliable mobile edge analytics in intelligent transportation systems"
    - "echo state networks for self organizing resource allocation in lte u with uplink downlink decoupling"
    - "lstm a search space odyssey"
    - "deep learning based dynamic watermarking for secure signal authentication in the internet of things"
    - "artificial neural networks based machine learning for wireless networks a tutorial"
    - "coordinated dynamic spectrum management of lte u and wi fi networks"
    - "learning to cooperate via policy search"
    - "long short term memory based recurrent neural network architectures for large vocabulary speech recognition"

Connected through hyperedge HE-7 (induced by source paper "backhaul for low altitude uavs in urban environments"):
  with members:
    - "the sky is not the limit lte for unmanned aerial vehicles"
    - "modeling and analyzing millimeter wave cellular systems"
    - "coverage analysis for low altitude uav networks in urban environments"
    - "coexistence of terrestrial and aerial users in cellular networks"
    - "a stochastic geometry model of backhaul and user coverage in urban uav networks"
    - "downlink and uplink cell association with traditional macrocells and millimeter wave small cells"
    - "fso based vertical backhaul fronthaul framework for 5g wireless networks"
    - "enabling uav cellular with millimeter wave communication potentials and approaches"

Connected through hyperedge HE-8 (induced by source paper "network formation in the sky unmanned aerial vehicles for multi hop wireless backhauling"):
  with members:
    - "efficient deployment of multiple unmanned aerial vehicles for optimal wireless coverage"
    - "caching in the sky proactive deployment of cache enabled unmanned aerial vehicles for optimized quality of experience"
    - "fso based vertical backhaul fronthaul framework for 5g wireless networks"
    - "wireless communications with unmanned aerial vehicles opportunities and challenges"
    - "unmanned aerial vehicle with underlaid device to device communications performance and tradeoffs"
    - "matching theory for backhaul management in small cell networks with mmwave capabilities"
    - "network formation games among relay stations in next generation wireless networks"

Question: Please tell me which class the center node belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.GT(Computer Science and Game Theory)