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 "large scale convex optimization for dense wireless cooperative networks"):
  Abstract: Convex optimization is a powerful tool for resource allocation and signal processing in wireless networks. As the network density is expected to drastically increase in order to accommodate the exponentially growing mobile data traffic, performance optimization problems are entering a new era characterized by a high dimension and/or a large number of constraints, which poses significant design and computational challenges. In this paper, we present a novel two-stage approach to solve large-scale convex optimization problems for dense wireless cooperative networks, which can effectively detect infeasibility and enjoy modeling flexibility. In the proposed approach, the original large-scale convex problem is transformed into a standard cone programming form in the first stage via matrix stuffing, which only needs to copy the problem parameters such as channel state information (CSI) and quality-of-service (QoS) requirements to the prestored structure of the standard form. The capability of yielding infeasibility certificates and enabling parallel computing is achieved by solving the homogeneous self-dual embedding of the primal-dual pair of the standard form. In the solving stage, the operator splitting method, namely, the alternating direction method of multipliers (ADMM), is adopted to solve the large-scale homogeneous self-dual embedding. Compared with second-order methods, ADMM can solve large-scale problems in parallel with modest accuracy within a reasonable amount of time. Simulation results will demonstrate the speedup, scalability, and reliability of the proposed framework compared with the state-of-the-art modeling frameworks and solvers.

Includes members:
  Member "robust group sparse beamforming for multicast green cloud ran with imperfect csi":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "secure millimeter wave cloud radio access networks relying on microwave multicast fronthaul")
  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 "optimal multiuser transmit beamforming a difficult problem with a simple solution structure":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "massive mimo has unlimited capacity")
  Member "scalable coordinated beamforming for dense wireless cooperative networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "low complexity antenna selection for low target rate users in dense cloud radio access networks")
  Member "base station activation and linear transceiver design for optimal resource management in heterogeneous networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "power efficient resource allocation in c rans with sinr constraints and deadlines")
  Member "group sparse beamforming for green cloud ran":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sparse optimization for green edge ai inference")
  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 "scaling up mimo opportunities and challenges with very large arrays":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "downlink secrecy rate of one bit massive mimo system with active eavesdropping")

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)