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: "joint user association and reuse pattern selection in heterogeneous networks"
  Abstract: The successful deployment of LTE heterogeneous networks (HetNets) depends crucially on the inter-cell interference (ICI) management. Among ICI coordination schemes, fractional frequency reuse (FFR) is considered as an efficient technique well-suited to OFDMA-based HetNets. Two coupled questions in this context are: 1) how to associate users to appropriate base-stations considering the long list of available candidate cells, and 2) how to allocate frequency resources among multiple cells. In this paper, we treat the multi-cell frequency allocation as frequency partitioning among multiple reuse patterns, and develop a novel algorithm to solve these two coupled questions in a joint manner. We also provide practical criterion to select the set of essential candidate patterns from all possible patterns. Results show that the proposed joint strategy improves both the cell-edge user and overall network throughput.

Connected through hyperedge HE-1 (induced by source paper "scalable spectrum allocation for large networks based on 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"
    - "energy efficient cell activation user association and spectrum allocation in heterogeneous networks"
    - "traffic driven spectrum allocation in heterogeneous networks"
    - "distributed linear precoder optimization and base station selection for an uplink heterogeneous network"

Connected through hyperedge HE-2 (induced by source paper "energy management in heterogeneous networks with cell activation user association and interference coordination"):
  with members:
    - "network topology adaptation and interference coordination for energy saving in heterogeneous networks"
    - "energy efficient cell activation user association and spectrum allocation in heterogeneous networks"
    - "traffic driven spectrum allocation in heterogeneous networks"
    - "base station activation and linear transceiver design for optimal resource management in heterogeneous networks"
    - "optimal joint user association and resource allocation in heterogeneous networks via sparsity pursuit"
    - "group sparse beamforming for green cloud ran"
    - "base station selection for energy efficient network operation with the majorization minimization algorithm"
    - "user association for load balancing in heterogeneous cellular networks"

Connected through hyperedge HE-3 (induced by source paper "network topology adaptation and interference coordination for energy saving in heterogeneous networks"):
  with members:
    - "base station selection for energy efficient network operation with the majorization minimization algorithm"

Connected through hyperedge HE-4 (induced by source paper "optimal joint user association and resource allocation in heterogeneous networks via sparsity pursuit"):
  with members:
    - "joint downlink base station association and power control for max min fairness computation and complexity"
    - "traffic driven spectrum allocation in heterogeneous networks"
    - "algorithms for enhanced inter cell interference coordination eicic in lte hetnets"
    - "an overview of load balancing in hetnets old myths and open problems"
    - "distributed pricing based user association for downlink heterogeneous cellular networks"
    - "on off macrocells and load balancing in heterogeneous cellular networks"
    - "offloading in heterogeneous networks modeling analysis and design insights"
    - "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)