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: "on the fronthaul statistical multiplexing gain"
  Abstract: Breaking the fronthaul capacity limitations is vital to make cloud radio access network (C-RAN) scalable and practical. One promising way is aggregating several remote radio units (RRUs) as a cluster to share a fronthaul link, so as to enjoy the statistical multiplexing gain brought by the spatial randomness of the traffic. In this letter, a tractable model is proposed to analyze the fronthaul statistical multiplexing gain. We first derive the user blocking probability caused by the limited fronthaul capacity, including its upper and lower bounds. We then obtain the limits of fronthaul statistical multiplexing gain when the cluster size approaches infinity. Analytical results reveal that the user blocking probability decreases exponentially with the average fronthaul capacity per RRU, and the exponent is proportional to the cluster size. Numerical results further show considerable fronthaul statistical multiplexing gain even at a small to medium cluster size.

Connected through hyperedge HE-1 (induced by source paper "flexible functional split and power control for energy harvesting cloud radio access networks"):
  with members:
    - "optimal placement of baseband functions for energy harvesting virtual small cells"
    - "online ski rental for on off scheduling of energy harvesting base stations"
    - "greendelivery proactive content caching and push with energy harvesting based small cells"
    - "redesigning fronthaul for next generation networks beyond baseband samples and point to point links"
    - "5g wireless backhaul networks challenges and research advances"
    - "energy harvesting broadband communication systems with processing energy cost"
    - "optimal power allocation for energy harvesting and power grid coexisting wireless communication systems"
    - "transmission with energy harvesting nodes in fading wireless channels optimal policies"

Connected through hyperedge HE-2 (induced by source paper "r fft function split at ifft fft in unified lte cran and cable access network"):
  with members:
    - "recent advances in cloud radio access networks system architectures key techniques and open issues"
    - "cluster content caching an energy efficient approach to improve quality of service in cloud radio access networks"
    - "statistical multiplexing gain analysis of heterogeneous virtual base station pools in cloud radio access networks"
    - "software defined optical networks sdons a comprehensive survey"
    - "upstream polling protocols for flow control in pon xdsl hybrid access networks"
    - "green ofdma resource allocation in cache enabled cran"
    - "optimized configurable architectures for scalable soft input soft output mimo detectors with 256 qam"
    - "quantized massive mu mimo ofdm uplink"

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)