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 "group sparse precoding for cloud ran with multiple user antennas"):
  Abstract: Cloud radio access network (C-RAN) has become a promising network architecture to support the massive data traffic in the next generation cellular networks. In a C-RAN, a massive number of low-cost remote antenna ports (RAPs) are connected to a single baseband unit (BBU) pool via high-speed low-latency fronthaul links, which enables efficient resource allocation and interference management. As the RAPs are geographically distributed, group sparse beamforming schemes attract extensive studies, where a subset of RAPs is assigned to be active and a high spectral efficiency can be achieved. However, most studies assume that each user is equipped with a single antenna. How to design the group sparse precoder for the multiple antenna users remains little understood, as it requires the joint optimization of the mutual coupling transmit and receive beamformers. This paper formulates an optimal joint RAP selection and precoding design problem in a C-RAN with multiple antennas at each user. Specifically, we assume a fixed transmit power constraint for each RAP, and investigate the optimal tradeoff between the sum rate and the number of active RAPs. Motivated by the compressive sensing theory, this paper formulates the group sparse precoding problem by inducing the     l 0    -norm as a penalty and then uses the reweighted     l 1     heuristic to find a solution. By adopting the idea of block diagonalization precoding, the problem can be formulated as a convex optimization, and an efficient algorithm is proposed based on its Lagrangian dual. Simulation results verify that our proposed algorithm can achieve almost the same sum rate as that obtained from an exhaustive search.

Includes members:
  Member "joint precoding and rrh selection for user centric green mimo c ran":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hybrid precoder design for cache enabled millimeter wave radio access networks")
  Member "energy efficiency of downlink transmission strategies for cloud radio access networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sparse optimization for green edge ai inference")
  Member "a survey of sparse representation algorithms and applications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "joint power allocation and load balancing optimization for energy efficient cell free massive mimo networks")
  Member "robust group sparse beamforming for multicast green cloud ran with imperfect csi":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "power efficient resource allocation in c rans with sinr constraints and deadlines")
  Member "a comparative study of downlink mimo cellular networks with co located and distributed base station antennas":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "power minimization in distributed antenna systems using non orthogonal multiple access and mutual successive interference cancellation")
  Member "sparse beamforming and user centric clustering for downlink cloud radio access network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "secure millimeter wave cloud radio access networks relying on microwave multicast fronthaul")
  Member "group sparse beamforming for green cloud ran":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sparse optimization for green edge ai inference")

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)