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 joint resource allocation and power control for d2d communications"
  Abstract: In this paper, joint resource allocation and power control for energy efficient device-to-device (D2D) communications underlaying cellular networks are investigated. The resource and power are optimized for maximization of the energy efficiency (EE) of D2D communications. Exploiting the properties of fractional programming, we transform the original nonconvex optimization problem in fractional form into an equivalent optimization problem in subtractive form. Then, an efficient iterative resource allocation and power control scheme is proposed. In each iteration, part of the constraints of the EE optimization problem is removed by exploiting the penalty function approach. We further propose a novel two-layer approach which allows to find the optimum at each iteration by decoupling the EE optimization problem of joint resource allocation and power control into two separate steps. In the first layer, the optimal power values are obtained by solving a series of maximization problems through root-finding with or without considering the loss of cellular users' rates. In the second layer, the formulated optimization problem belongs to a classical resource allocation problem with single allocation format which admits a network flow formulation so that it can be solved to optimality. Simulation results demonstrate the remarkable improvements in terms of EE by using the proposed iterative resource allocation and power control scheme.

Connected through hyperedge HE-1 (induced by source paper "learning to branch accelerating resource allocation in wireless networks"):
  with members:
    - "learning to optimize training deep neural networks for interference management"
    - "multi tenant cross slice resource orchestration a deep reinforcement learning approach"
    - "deep reinforcement learning based resource allocation for v2v communications"
    - "radio resource allocation for device to device underlay communication using hypergraph theory"
    - "a survey on device to device communication in cellular networks"
    - "a reduction of imitation learning and structured prediction to no regret online learning"

Connected through hyperedge HE-2 (induced by source paper "joint transmitter and receiver design for pattern division multiple access"):
  with members:
    - "pattern division multiple access with large scale antenna array"
    - "a survey on non orthogonal multiple access for 5g networks research challenges and future trends"
    - "design of massive mimo noma with limited feedback"
    - "advanced coordinated beamforming for the downlink of future lte cellular networks"
    - "on the spectral efficiency and security enhancements of noma assisted multicast unicast streaming"
    - "an overview of transmission theory and techniques of large scale antenna systems for 5g wireless communications"
    - "the application of mimo to non orthogonal multiple access"
    - "uplink contention based scma for 5g radio access"

Connected through hyperedge HE-3 (induced by source paper "power control via stackelberg game for small cell networks"):
  with members:
    - "energy efficient noncooperative power control in small cell networks"
    - "price based resource allocation for spectrum sharing femtocell networks a stackelberg game approach"

Connected through hyperedge HE-4 (induced by source paper "resource allocation in cloud radio access networks with device to device communications"):
  with members:
    - "energy efficient resource allocation optimization for multimedia heterogeneous cloud radio access networks"
    - "fog computing based radio access networks issues and challenges"
    - "recent advances in cloud radio access networks system architectures key techniques and open issues"
    - "energy efficient joint congestion control and resource optimization in heterogeneous cloud radio access networks"
    - "queue aware energy efficient joint remote radio head activation and beamforming in cloud radio access networks"
    - "inter tier interference suppression in heterogeneous cloud radio access networks"
    - "fronthaul constrained cloud radio access networks insights and challenges"
    - "system architecture and key technologies for 5g heterogeneous cloud radio access networks"

Connected through hyperedge HE-5 (induced by source paper "pilot reuse among d2d users in d2d underlaid massive mimo systems"):
  with members:
    - "graph coloring based pilot allocation to mitigate pilot contamination for multi cell massive mimo systems"
    - "energy efficient resource allocation for device to device underlay communication"
    - "an uplink interference analysis for massive mimo systems with mrc and zf receivers"
    - "pilot reuse for massive mimo transmission over spatially correlated rayleigh fading channels"
    - "the interplay between massive mimo and underlaid d2d networking"
    - "a survey on device to device communication in cellular networks"
    - "asymptotic coverage probability and rate in massive mimo networks"
    - "a coordinated approach to channel estimation in large scale multiple antenna systems"

Connected through hyperedge HE-6 (induced by source paper "energy efficient noncooperative power control in small cell networks"):
  with members:
    - "a game theoretic analysis for energy efficient heterogeneous networks"
    - "framework for link level energy efficiency optimization with informed transmitter"
    - "power control in two tier femtocell networks"
    - "interference and outage in clustered wireless ad hoc networks"

Connected through hyperedge HE-7 (induced by source paper "device to device communication in cellular networks"):
  with members:
    - "exploiting device to device communications to enhance spatial reuse for popular content downloading in directional mmwave small cells"
    - "a survey on device to device communication in cellular networks"
    - "cognitive and energy harvesting based d2d communication in cellular networks stochastic geometry modeling and analysis"
    - "low rate machine type communication via wireless device to device d2d links"
    - "base station assisted device to device communications for high throughput wireless video networks"
    - "power control for d2d underlaid cellular networks modeling algorithms and analysis"

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)