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: "relay selection and power allocation in cooperative cellular networks"
  Abstract: We consider a system with a single base station communicating with multiple users over orthogonal channels while being assisted by multiple relays. Several recent works have suggested that, in such a scenario, selection, i.e., a single relay helping the source, is the best relaying option in terms of the resulting complexity and overhead. However, in a multiuser setting, optimal relay assignment is a combinatorial problem. In this paper, we formulate a related convex optimization problem that provides an extremely tight upper bound on performance and show that selection is, almost always, inherent in the solution. We also provide a heuristic to find a close-to-optimal relay assignment and power allocation across users supported by a single relay. Simulation results using realistic channel models demonstrate the efficacy of the proposed schemes, but also raise the question as to whether the gains from relaying are worth the additional costs.

Connected through hyperedge HE-1 (induced by source paper "matching based two timescale resource allocation for cooperative d2d communication"):
  with members:
    - "learning for matching game in cooperative d2d communication with incomplete information"
    - "cooperative spectrum sharing between d2d users and edge users a matching theory perspective"
    - "a survey on device to device communication in cellular networks"
    - "spectrum leasing as an incentive towards uplink macrocell and femtocell cooperation"

Connected through hyperedge HE-2 (induced by source paper "joint millimeter wave fronthaul and ofdma resource allocation in ultra dense cran"):
  with members:
    - "locally orthogonal training design for cloud rans based on graph coloring"
    - "millimeter wave wireless communications new results for rural connectivity"
    - "content centric sparse multicast beamforming for cache enabled cloud ran"
    - "wireless communications in the era of big data"
    - "5g ultra dense cellular networks"
    - "large scale convex optimization for ultra dense cloud ran"
    - "optimized uplink transmission in multi antenna c ran with spatial compression and forward"
    - "optimized backhaul compression for uplink cloud radio access network"

Connected through hyperedge HE-3 (induced by source paper "optimal distributed resource allocation for decode and forward relay networks"):
  with members:
    - "joint bandwidth and power allocation with admission control in wireless multi user networks with and without relaying"
    - "distributive subband allocation power and rate control for relay assisted ofdma cellular system with imperfect system state knowledge"
    - "relay assisted f tdma ad hoc networks node classification power allocation and relaying strategies"

Connected through hyperedge HE-4 (induced by source paper "wsr maximized resource allocation in multiple df relays aided ofdma downlink transmission"):
  with members:
    - "sum rate maximized resource allocation in multiple df relays aided ofdm transmission"
    - "distributive subband allocation power and rate control for relay assisted ofdma cellular system with imperfect system state knowledge"

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)