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: "the 5g cellular backhaul management dilemma to cache or to serve"
  Abstract: With the introduction of caching capabilities into small cell networks (SCNs), new backaul management mechanisms need to be developed to prevent the predicted files that are downloaded by the at the small base stations (SBSs) to be cached from jeopardizing the urgent requests that need to be served via the backhaul. Moreover, these mechanisms must account for the heterogeneity of the backhaul that will be encompassing both wireless backhaul links at various frequency bands and a wired backhaul component. In this paper, the heterogeneous backhaul management problem is formulated as a minority game in which each SBS has to define the number of predicted files to download, without affecting the required transmission rate of the current requests. For the formulated game, it is shown that a unique fair proper mixed Nash equilibrium (PMNE) exists. Self-organizing reinforcement learning algorithm is proposed and proved to converge to a unique Boltzmann-Gibbs equilibrium which approximates the desired PMNE. Simulation results show that the performance of the proposed approach can be close to that of the ideal optimal algorithm while it outperforms a centralized greedy approach in terms of the amount of data that is cached without jeopardizing the quality-of-service of current requests.

Connected through hyperedge HE-1 (induced by source paper "security of 5g mobile backhaul networks a survey"):
  with members:
    - "bandwidth partitioning and downlink analysis in mmwave integrated access and backhaul for 5g"
    - "cross layer authentication protocol design for ultra dense 5g hetnets"
    - "formal analysis of 5g authentication"
    - "massive multiple input massive multiple output for 5g wireless backhauling"
    - "a survey of physical layer security techniques for 5g wireless networks and challenges ahead"
    - "optimal joint routing and scheduling in millimeter wave cellular networks"
    - "a survey on low latency towards 5g ran core network and caching solutions"
    - "fso based vertical backhaul fronthaul framework for 5g 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"
    - "echo state networks for self organizing resource allocation in lte u with uplink downlink decoupling"
    - "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"

Connected through hyperedge HE-4 (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:
    - "echo state networks for self organizing resource allocation in lte u with uplink downlink decoupling"
    - "caching meets millimeter wave communications for enhanced mobility management in 5g networks"
    - "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"

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)