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: "queue aware energy efficient joint remote radio head activation and beamforming in cloud radio access networks"
  Abstract: In this paper, we study the stochastic optimization of cloud radio access networks (C-RANs) by joint remote radio head (RRH) activation and beamforming in the downlink. Unlike most previous works that only consider a static optimization framework with full traffic buffers, we formulate a dynamic optimization problem by explicitly considering the effects of random traffic arrivals and time-varying channel fading. The stochastic formulation can quantify the tradeoff between power consumption and queuing delay. Leveraging on the Lyapunov optimization technique, the stochastic optimization problem can be transformed into a per-slot penalized weighted sum rate maximization problem, which is shown to be nondeterministic polynomial-time hard. Based on the equivalence between the penalized weighted sum rate maximization problem and the penalized weighted minimum mean square error (WMMSE) problem, the group sparse beamforming optimization-based WMMSE algorithm and the relaxed integer programming-based WMMSE algorithm are proposed to efficiently obtain the joint RRH activation and beamforming policy. Both algorithms can converge to a stationary solution with low-complexity and can be implemented in a parallel manner, thus they are highly scalable to large-scale C-RANs. In addition, these two proposed algorithms provide a flexible and efficient means to adjust the power-delay tradeoff on demand.

Connected through hyperedge HE-1 (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"
    - "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-2 (induced by source paper "energy efficient joint congestion control and resource optimization in heterogeneous cloud radio access networks"):
  with members:
    - "energy efficient resource allocation optimization for multimedia heterogeneous cloud radio access networks"
    - "inter tier interference suppression in heterogeneous cloud radio access networks"
    - "contract based interference coordination in heterogeneous cloud radio access networks"
    - "energy efficient resource assignment and power allocation 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"
    - "heterogeneous cloud radio access networks a new perspective for enhancing spectral and energy efficiencies"
    - "resource allocation optimization for delay sensitive traffic in fronthaul constrained cloud radio access networks"

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)