human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "robust group sparse beamforming for multicast green cloud ran with imperfect csi"):
  Abstract: In this paper, we investigate the network power minimization problem for the multicast cloud radio access network (Cloud-RAN) with imperfect channel state information (CSI). The key observation is that network power minimization can be achieved by adaptively selecting active remote radio heads (RRHs) via controlling the group-sparsity structure of the beamforming vector. However, this yields a non-convex combinatorial optimization problem, for which we propose a three-stage robust group sparse beamforming algorithm. In the first stage, a quadratic variational formulation of the weighted mixed l#N# 1#N#/l#N# 2#N#-norm is proposed to induce the group-sparsity structure in the aggregated beamforming vector, which indicates those RRHs that can be switched off. A perturbed alternating optimization algorithm is then proposed to solve the resultant non-convex group-sparsity inducing optimization problem by exploiting its convex substructures. In the second stage, we propose a PhaseLift technique based algorithm to solve the feasibility problem with a given active RRH set, which helps determine the active RRHs. Finally, the semidefinite relaxation (SDR) technique is adopted to determine the robust multicast beamformers. Simulation results will demonstrate the convergence of the perturbed alternating optimization algorithm, as well as, the effectiveness of the proposed algorithm to minimize the network power consumption for multicast Cloud-RAN.

Includes members:
  Member "large scale convex optimization for dense wireless cooperative networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "large scale beamforming for massive mimo via randomized sketching")
  Member "large scale convex optimization for ultra dense cloud ran":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "power efficient resource allocation in c rans with sinr constraints and deadlines")
  Member "scalable coordinated beamforming for dense wireless cooperative networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "low complexity antenna selection for low target rate users in dense cloud radio access networks")
  Member "what will 5g be":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "5g network slicing using sdn and nfv a survey of taxonomy architectures and future challenges")
  Member "group sparse beamforming for green cloud ran":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sparse optimization for green edge ai inference")
  Member "joint precoding and multivariate backhaul compression for the downlink of cloud radio access networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "caching at base stations with multi cluster multicast wireless backhaul via accelerated first order algorithm")
  Member "optimal stochastic coordinated beamforming for wireless cooperative networks with csi uncertainty":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "energy efficient processing and robust wireless cooperative transmission for edge inference")
  Member "distributed robust multicell coordinated beamforming with imperfect csi an admm approach":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "robust urllc packet scheduling of ofdm systems")

Question: Please tell me which class the source paper 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)