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: "joint user selection and energy minimization for ultra dense multi channel c ran with incomplete csi"
  Abstract: This paper provides a unified framework to deal with the challenges arising in dense cloud radio access networks (C-RAN), which include huge power consumption, limited fronthaul capacity, heavy computational complexity, unavailability of full channel state information (CSI), etc. Specifically, we aim to jointly optimize the remote radio head (RRH) selection, user equipment (UE)-RRH associations and beam-vectors to minimize the total network power consumption (NPC) for dense multi-channel downlink C-RAN with incomplete CSI subject to per-RRH power constraints, each UE's total rate requirement, and fronthaul link capacity constraints. This optimization problem is NP-hard. In addition, due to the incomplete CSI, the exact expression of UEs' rate expression is intractable. We first conservatively replace UEs' rate expression with its lower-bound. Then, based on the successive convex approximation (SCA) technique and the relationship between the data rate and the mean square error (MSE), we propose a single-layer iterative algorithm to solve the NPC minimization problem with convergence guarantee. In each iteration of the algorithm, the Lagrange dual decomposition method is used to derive the structure of the optimal beam-vectors, which facilitates the parallel computations at the Baseband unit (BBU) pool. Furthermore, a bisection UE selection algorithm is proposed to guarantee the feasibility of the problem. Simulation results show the benefits of the proposed algorithms and the fact that a limited amount of CSI is sufficient to achieve performance close to that obtained when perfect CSI is possessed.

Connected through hyperedge HE-1 (induced by source paper "robust beamforming with pilot reuse scheduling in a heterogeneous cloud radio access network"):
  with members:
    - "joint precoding and rrh selection for user centric green mimo c ran"
    - "energy efficient resource allocation optimization for multimedia heterogeneous cloud radio access networks"
    - "robust pilot decontamination based on joint angle and power domain discrimination"
    - "energy efficient joint congestion control and resource optimization in heterogeneous cloud radio access networks"
    - "energy efficiency of downlink transmission strategies for cloud radio access networks"
    - "contract based interference coordination in heterogeneous cloud radio access networks"
    - "coordinated shared spectrum precoding with distributed csit"
    - "energy efficient resource assignment and power allocation in heterogeneous cloud radio access networks"

Connected through hyperedge HE-2 (induced by source paper "boosting fronthaul capacity global optimization of power sharing for centralized radio access network"):
  with members:
    - "differential evolution algorithm aided turbo channel estimation and multi user detection for g fast systems in the presence of fext"
    - "regularized zero forcing precoding aided adaptive coding and modulation for large scale antenna array based air to air communications"
    - "joint precoding and rrh selection for user centric green mimo c ran"
    - "overview of millimeter wave communications for fifth generation 5g wireless networks with a focus on propagation models"
    - "adaptive coding and modulation for large scale antenna array based aeronautical communications in the presence of co channel interference"
    - "multivariate fronthaul quantization for downlink c ran"
    - "fronthauling for 5g lte u ultra dense cloud small cell networks"
    - "cluster content caching an energy efficient approach to improve quality of service in 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.NI(Networking and Internet Architecture)