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: "echo state networks for self organizing resource allocation in lte u with uplink downlink decoupling"
  Abstract: Uplink–downlink decoupling in which users can be associated to different base stations in the uplink and downlink of heterogeneous small cell networks (SCNs) has attracted significant attention recently. However, most existing works focus on simple association mechanisms in LTE SCNs that operate only in the licensed band. In contrast, in this paper, the problem of resource allocation with uplink–downlink decoupling is studied for an SCN that incorporates LTE in the unlicensed band. Here, the users can access both licensed and unlicensed bands while being associated to different base stations. This problem is formulated as a noncooperative game that incorporates user association, spectrum allocation, and load balancing. To solve this problem, a distributed algorithm based on the machine learning framework of  echo state networks  (ESNs) is proposed. This proposed algorithm allows the small base stations to autonomously choose their optimal resource allocation strategies given only limited information on the network’s and users’ states. It is shown that the proposed algorithm converges to a stationary mixed-strategy distribution, which constitutes a mixed strategy Nash equilibrium for their studied game. Simulation results show that the proposed approach yields significant gain, in terms of the sum-rate of the 50th percentile of users, that reaches up to 167% compared with a Q-learning algorithm. The results also show that the ESN significantly provides a considerable reduction of information exchange for the wireless network.

Connected through hyperedge HE-1 (induced by source paper "20 years of evolution from cognitive to intelligent communications"):
  with members:
    - "spectrum sharing in vehicular networks based on multi agent reinforcement learning"
    - "toward intelligent vehicular networks a machine learning framework"
    - "deep learning in physical layer communications"
    - "deep reinforcement learning based resource allocation for v2v communications"
    - "deep reinforcement learning based modulation and coding scheme selection in cognitive heterogeneous networks"
    - "sub nyquist cyclostationary detection for cognitive radio"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "deep reinforcement learning multiple access for heterogeneous wireless networks"

Connected through hyperedge HE-2 (induced by source paper "application of machine learning in wireless networks key techniques and open issues"):
  with members:
    - "interference management for cellular connected uavs a deep reinforcement learning approach"
    - "learning to optimize training deep neural networks for interference management"
    - "deep reinforcement learning for resource management in network slicing"
    - "optimized computation offloading performance in virtual edge computing systems via deep reinforcement learning"
    - "deep learning for radio resource allocation in multi cell networks"
    - "multi tenant cross slice resource orchestration a deep reinforcement learning approach"
    - "deep reinforcement learning based mode selection and resource management for green fog radio access networks"
    - "deep learning in mobile and wireless networking a survey"

Connected through hyperedge HE-3 (induced by source paper "liquid state machine learning for resource and cache management in lte u unmanned aerial vehicle uav networks"):
  with members:
    - "beyond 5g with uavs foundations of a 3d wireless cellular network"
    - "uav aided offloading for cellular hotspot"
    - "joint altitude beamwidth location and bandwidth optimization for uav enabled communications"
    - "wireless communication using unmanned aerial vehicles uavs optimal transport theory for hover time optimization"
    - "joint trajectory and communication design for multi uav enabled wireless networks"
    - "virtual reality over wireless networks quality of service model and learning based resource management"
    - "uav air to ground channel characterization for mmwave systems"
    - "power control in uav supported ultra dense networks communications caching and energy transfer"

Connected through hyperedge HE-4 (induced by source paper "contract based incentive mechanism for lte over unlicensed channels"):
  with members:
    - "orla olaa orthogonal coexistence of laa and wifi in unlicensed spectrum"
    - "network formation in the sky unmanned aerial vehicles for multi hop wireless backhauling"
    - "proactive resource management in lte u systems a deep learning perspective"
    - "the impact of unlicensed access on small cell resource allocation"
    - "breaking the economic barrier of caching in cellular networks incentives and contracts"
    - "caching in the sky proactive deployment of cache enabled unmanned aerial vehicles for optimized quality of experience"
    - "auction based coopetition between lte unlicensed and wi fi"
    - "joint caching and pricing strategies for popular content in information centric networks"

Connected through hyperedge HE-5 (induced by source paper "collaborative artificial intelligence ai for user cell association in ultra dense cellular systems"):
  with members:
    - "an introduction to deep learning for the physical layer"
    - "optimal transport theory for cell association in uav enabled cellular networks"
    - "cognitive hierarchy theory for distributed resource allocation in the internet of things"
    - "artificial neural networks based machine learning for wireless networks a tutorial"
    - "distributed user association in energy harvesting dense small cell networks a mean field multi armed bandit approach"
    - "downlink and uplink cell association with traditional macrocells and millimeter wave small cells"
    - "caching in the sky proactive deployment of cache enabled unmanned aerial vehicles for optimized quality of experience"
    - "learning how to communicate in the internet of things finite resources and heterogeneity"

Connected through hyperedge HE-6 (induced by source paper "echo liquid state deep learning for 360 circ content transmission and caching in wireless vr networks with cellular connected uavs"):
  with members:
    - "caching meets millimeter wave communications for enhanced mobility management in 5g networks"
    - "the 5g cellular backhaul management dilemma to cache or to serve"
    - "caching in the sky proactive deployment of cache enabled unmanned aerial vehicles for optimized quality of experience"
    - "cluster content caching an energy efficient approach to improve quality of service in cloud radio access networks"
    - "joint caching and pricing strategies for popular content in information centric networks"
    - "unmanned aerial vehicle with underlaid device to device communications performance and tradeoffs"

Connected through hyperedge HE-7 (induced by source paper "ruin theory for dynamic spectrum allocation in lte u networks"):
  with members:
    - "proactive resource management in lte u systems a deep learning perspective"
    - "licensed and unlicensed spectrum allocation in heterogeneous networks"

Connected through hyperedge HE-8 (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"
    - "lstm a search space odyssey"
    - "inter operator resource management for millimeter wave multi hop backhaul networks"
    - "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"

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.IT(Information Theory)