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: "dynamic power control for delay aware device to device communications"
  Abstract: In this paper, we consider the dynamic power control for delay-aware D2D communications. The stochastic optimization problem is formulated as an infinite horizon average cost Markov decision process. To deal with the curse of dimensionality, we utilize the interference filtering property of the CSMA-like MAC protocol and derive a closed-form approximate priority function and the associated error bound using perturbation analysis. Based on the closed-form approximate priority function, we propose a low-complexity power control algorithm solving the per-stage optimization problem. The proposed solution is further shown to be asymptotically optimal for a sufficiently large carrier sensing distance. Finally, the proposed power control scheme is compared with various baselines through simulations, and it is shown that significant performance gain can be achieved.

Connected through hyperedge HE-1 (induced by source paper "unified scheduling for predictable communication reliability in cellular networks with d2d links"):
  with members:
    - "distributed resource allocation in device to device enhanced cellular networks"
    - "a survey on delay aware resource control for wireless systems large deviation theory stochastic lyapunov drift and distributed stochastic learning"

Connected through hyperedge HE-2 (induced by source paper "queue aware energy efficient control for dense wireless networks"):
  with members:
    - "low complexity delay constrained beamforming for multi user mimo systems with imperfect csit"

Connected through hyperedge HE-3 (induced by source paper "energy efficiency in mimo underlay and overlay device to device communications and cognitive radio systems"):
  with members:
    - "energy efficient power control a look at 5g wireless technologies"
    - "energy efficiency and sum rate when massive mimo meets device to device communication"
    - "energy efficient resource allocation for device to device underlay communication"
    - "resource allocation under channel uncertainties for relay aided device to device communication underlaying lte a cellular networks"
    - "energy efficiency and spectral efficiency tradeoff in device to device d2d communications"
    - "designing multi user mimo for energy efficiency when is massive mimo the answer"
    - "cognitive beamforming for multiple secondary data streams with individual snr constraints"
    - "efficiency resource allocation for device to device underlay communication systems a reverse iterative combinatorial auction based 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 joint resource allocation and power control for d2d communications"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "delay aware uplink fronthaul allocation in cloud radio access networks"):
  with members:
    - "full duplex cloud radio access networks an information theoretic viewpoint"
    - "optimized backhaul compression for uplink cloud radio access network"
    - "joint power control and fronthaul rate allocation for throughput maximization in ofdma based cloud radio access network"
    - "joint precoding and multivariate backhaul compression for the downlink of cloud radio access networks"
    - "multihop backhaul compression for the uplink of cloud radio access networks"
    - "a survey on delay aware resource control for wireless systems large deviation theory stochastic lyapunov drift and distributed stochastic learning"
    - "queue aware dynamic clustering and power allocation for network mimo systems via distributed stochastic learning"
    - "distributive stochastic learning for delay optimal ofdma power and subband allocation"

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)