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: "multiuser joint energy bandwidth allocation with energy harvesting part ii multiple broadcast channels proportional fairness"
  Abstract: AbstractIn this paper, we consider the energy-bandwidth allocationfor a network with multiple broadcastchannels, where the transmitters access the network orthogonally on the assigned frequency band andeach transmitter communicates with multiple receivers orthogonally or non-orthogonally.We assume thatthe energy harvesting state and channel gain of each transmitter can be predicted for K slots a priori.To maximize the weighted throughput, we formulate an optimization problem with O(MK)constraints,where M is the number of the receivers, and decompose it into the energy and bandwidth allocationsubproblems. In order to use the iterative algorithm proposed in [1] to solve the problem, we proposeefﬁcient algorithms to solve the two subproblems, so that th e optimal energy-bandwidth allocation canbe obtained with an overall complexity of O(MK 2 ), even though the problem is non-convex whenthe broadcast channel is non-orthogonal. For the orthogonal broadcast channel, we further formulate aproportionally-fair (PF) throughput maximization problem and derive the equivalence conditions suchthat the optimal solution can be obtained by solving a weighted throughput maximization problem.Further, the algorithm to obtain the proper weights is proposed. Simulation results show that theproposed algorithm can make efﬁcient use of the harvested en ergy and the available bandwidth, andachieve signiﬁcantly better performancethan some heurist ic policies for energy and bandwidth allocation.Moreover, it is seen that with energy-harvesting transmitters, non-orthogonal broadcast offers limitedgain over orthogonal broadcast.Index TermsConvex optimization, energy-bandwidth allocation, energy harvesting, non-orthogonal broadcast,orthogonal broadcast, proportionally fair scheduling.

Connected through hyperedge HE-1 (induced by source paper "grid energy consumption and qos tradeoff in hybrid energy supply wireless networks"):
  with members:
    - "transmit power minimization for wireless networks with energy harvesting relays"
    - "energy harvesting wireless communications a review of recent advances"
    - "comp meets smart grid a new communication and energy cooperation paradigm"
    - "energy harvesting small cell networks feasibility deployment and operation"
    - "a lyapunov optimization approach for green cellular networks with hybrid energy supplies"
    - "cost minimization for fading channels with energy harvesting and conventional energy"
    - "power allocation for conventional and buffer aided link adaptive relaying systems with energy harvesting nodes"
    - "multiuser joint energy bandwidth allocation with energy harvesting part i optimum algorithm multiple point to point channels"

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)