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: "energy efficient cell activation user association and spectrum allocation in heterogeneous networks"
  Abstract: Next generation (5G) cellular networks are expected to be supported by an extensive infrastructure with many-fold increase in the number of cells per unit area compared to today. The total energy consumption of base transceiver stations (BTSs) is an important issue for both economic and environmental reasons. In this paper, an optimization-based framework is proposed for energy-efficient global radio resource management in heterogeneous wireless networks. Specifically, with stochastic arrivals of known rates intended for users, the smallest set of BTSs is activated with jointly optimized user association and spectrum allocation to stabilize the network first and then minimize the delay. The scheme can be carried out periodically on a relatively slow timescale to adapt to aggregate traffic variations and average channel conditions. Numerical results show that the proposed scheme significantly reduces the energy consumption and increases the quality of service compared to existing schemes in the literature.

Connected through hyperedge HE-1 (induced by source paper "reinforcement learning based vehicle cell association algorithm for highly mobile millimeter wave communication"):
  with members:
    - "vehicular edge computing via deep reinforcement learning"
    - "wireless edge computing with latency and reliability guarantees"
    - "learning to optimize training deep neural networks for interference management"
    - "federated learning for ultra reliable low latency v2v communications"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"
    - "ultra reliable and low latency wireless communication tail risk and scale"
    - "user association and load balancing for massive mimo through deep learning"
    - "load balancing user association in millimeter wave mimo networks"

Connected through hyperedge HE-2 (induced by source paper "resource allocation in green dense cellular networks complexity and algorithms"):
  with members:
    - "large scale spectrum allocation for cellular networks via sparse optimization"
    - "optimal user scheduling in energy harvesting wireless networks"
    - "scalable spectrum allocation and user association in networks with many small cells"
    - "boost base station on off switching strategy for energy efficient massive mimo hetnets"
    - "online ski rental for on off scheduling of energy harvesting base stations"
    - "online energy efficient packet scheduling for a common deadline with and without energy harvesting"
    - "gate greening at the edges"
    - "optimizing user association and spectrum allocation in hetnets a utility perspective"

Connected through hyperedge HE-3 (induced by source paper "drag deep reinforcement learning based base station activation in heterogeneous networks"):
  with members:
    - "parameter space noise for exploration"
    - "deep reinforcement learning in large discrete action spaces"
    - "continuous control with deep reinforcement learning"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "deep reinforcement learning in parameterized action space"
    - "deep reinforcement learning with double q learning"
    - "downlink performance of cellular systems with base station sleeping user association and scheduling"
    - "base station activation and linear transceiver design for optimal resource management in heterogeneous networks"

Connected through hyperedge HE-4 (induced by source paper "a centralized metropolitan scale radio resource management scheme"):
  with members:
    - "hybrid spectrum sharing in mmwave cellular networks"
    - "the impact of unlicensed access on small cell resource allocation"
    - "energy management in heterogeneous networks with cell activation user association and interference coordination"
    - "licensed and unlicensed spectrum allocation in heterogeneous networks"
    - "distributed pricing based user association for downlink heterogeneous cellular networks"
    - "base station activation and linear transceiver design for optimal resource management in heterogeneous networks"
    - "user association for load balancing in heterogeneous cellular networks"

Connected through hyperedge HE-5 (induced by source paper "large scale spectrum allocation for cellular networks via sparse optimization"):
  with members:
    - "scalable spectrum allocation and user association in networks with many small cells"
    - "energy management in heterogeneous networks with cell activation user association and interference coordination"
    - "licensed and unlicensed spectrum allocation in heterogeneous networks"
    - "distributed pricing based user association for downlink heterogeneous cellular networks"
    - "base station activation and linear transceiver design for optimal resource management in heterogeneous networks"
    - "distributed linear precoder optimization and base station selection for an uplink heterogeneous network"
    - "user association for load balancing in heterogeneous cellular networks"

Connected through hyperedge HE-6 (induced by source paper "energy efficient user association and power allocation in millimeter wave based ultra dense networks with energy harvesting base stations"):
  with members:
    - "backhaul aware user association and resource allocation for energy constrained hetnets"
    - "distributed association and relaying with fairness in millimeter wave networks"
    - "user association and interference management in massive mimo hetnets"
    - "efficient scheduling and power allocation for d2d assisted wireless caching networks"
    - "mmwave massive mimo based wireless backhaul for the 5g ultra dense network"
    - "matching theory for backhaul management in small cell networks with mmwave capabilities"
    - "downlink performance of cellular systems with base station sleeping user association and scheduling"
    - "an overview of load balancing in hetnets old myths and open problems"

Connected through hyperedge HE-7 (induced by source paper "on the performance of multi tier heterogeneous cellular networks with idle mode capability"):
  with members:
    - "ultra dense networks is there a limit to spatial spectrum reuse"
    - "on the performance of practical ultra dense networks the major and minor factors"
    - "study on the idle mode capability with los and nlos transmissions"
    - "towards 1 gbps ue in cellular systems understanding ultra dense small cell deployments"
    - "analysis on cache enabled wireless heterogeneous networks"
    - "heterogeneous cellular networks with flexible cell association a comprehensive downlink sinr analysis"
    - "user association for load balancing in heterogeneous cellular networks"
    - "coverage and economy of cellular networks with many base stations"

Connected through hyperedge HE-8 (induced by source paper "scalable spectrum allocation and user association in networks with many small cells"):
  with members:
    - "energy management in heterogeneous networks with cell activation user association and interference coordination"
    - "traffic driven spectrum allocation in heterogeneous networks"
    - "distributed pricing based user association for downlink heterogeneous cellular networks"
    - "base station activation and linear transceiver design for optimal resource management in heterogeneous networks"
    - "on association cells in random heterogeneous networks"
    - "distributed linear precoder optimization and base station selection for an uplink heterogeneous network"
    - "user association for load balancing in heterogeneous cellular 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.IT(Information Theory)