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: "optimal power and range adaptation for green broadcasting"
  Abstract: Improving energy efficiency is key to network providers maintaining profit levels and an acceptable carbon footprint in the face of rapidly increasing data traffic in cellular networks in the coming years. The energy-saving concept studied in this paper is the adaptation of a base station's (BS's) transmit power levels and coverage area according to channel conditions and traffic load. The traffic load in cellular networks exhibits significant fluctuations in both space and time, which can be exploited, through cell range adaptation, for energy saving. In this paper, we design short- and long-term BS power control (STPC and LTPC respectively) policies for the OFDMA-based downlink of a single-cell system, where bandwidth is dynamically and equally shared among a random number of mobile users (MUs). STPC is a function of all MUs' channel gains that maintains the required user-level quality of service (QoS), while LTPC (including BS on-off control) is a function of traffic density that minimizes the long-term energy consumption at the BS under a minimum throughput constraint. We first develop a power scaling law that relates the (short-term) average transmit power at BS with the given cell range and MU density. Based on this result, we derive the optimal (long-term) transmit adaptation policy by considering a joint range adaptation and LTPC problem. By identifying the fact that energy saving at BS essentially comes from two major energy saving mechanisms (ESMs), i.e. range adaptation and BS on-off power control, we propose low-complexity suboptimal schemes with various combinations of the two ESMs to investigate their impacts on system energy consumption. It is shown that when the network throughput is low, BS on-off power control is the most effective ESM, while when the network throughput is higher, range adaptation becomes more effective.

Connected through hyperedge HE-1 (induced by source paper "system level modeling and optimization of the energy efficiency in cellular networks a stochastic geometry framework"):
  with members:
    - "effect of densification on cellular network performance with bounded pathloss model"
    - "modeling and analysis of cellular networks using stochastic geometry a tutorial"
    - "enhanced cellular coverage and throughput using rateless codes"
    - "area spectral efficiency analysis and energy consumption minimization in multiantenna poisson distributed networks"
    - "stochastic geometric analysis of energy efficient dense cellular networks"
    - "the intensity matching approach a tractable stochastic geometry approximation to system level analysis of cellular networks"
    - "a survey of energy efficient techniques for 5g networks and challenges ahead"
    - "spatial spectrum and energy efficiency of random cellular networks"

Connected through hyperedge HE-2 (induced by source paper "green 5g heterogeneous networks through dynamic small cell operation"):
  with members:
    - "energy group buying with loading sharing for green cellular networks"
    - "dynamic base station operation in large scale green cellular networks"
    - "spatial throughput maximization of wireless powered communication networks"
    - "what will 5g be"
    - "joint energy and spectrum cooperation for cellular communication systems"
    - "offloading in heterogeneous networks modeling analysis and design insights"
    - "user association for load balancing in heterogeneous cellular networks"
    - "green cellular networks a survey some research issues and challenges"

Connected through hyperedge HE-3 (induced by source paper "dynamic base station operation in large scale green cellular networks"):
  with members:
    - "green 5g heterogeneous networks through dynamic small cell operation"
    - "energy group buying with loading sharing for green cellular networks"
    - "energy harvesting wireless communications a review of recent advances"
    - "comp meets smart grid a new communication and energy cooperation paradigm"
    - "a general utility optimization framework for energy harvesting based wireless communications"
    - "spatial throughput maximization of wireless powered communication networks"
    - "a lyapunov optimization approach for green cellular networks with hybrid energy supplies"
    - "quantifying potential energy efficiency gain in green cellular wireless networks"

Connected through hyperedge HE-4 (induced by source paper "downlink and uplink energy minimization through user association and beamforming in cloud ran"):
  with members:
    - "base station activation and linear transceiver design for optimal resource management in heterogeneous networks"
    - "joint precoding and multivariate backhaul compression for the downlink of cloud radio access networks"
    - "distributed linear precoder optimization and base station selection for an uplink heterogeneous network"
    - "user association for load balancing in heterogeneous cellular networks"
    - "achieving global optimality for weighted sum rate maximization in the k user gaussian interference channel with multiple antennas"
    - "optimization with sparsity inducing penalties"
    - "cooperative interference management with miso beamforming"

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)