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 "joint transmission with limited backhaul connectivity"):
  Abstract: Downlink beamforming techniques with low signaling overhead are proposed for joint processing coordinated (JP) multi-point transmission. The objective is to maximize the weighted sum rate within joint transmission clusters. As the considered weighted sum rate maximization is a non-convex problem, successive convex approximation techniques, based on weighted mean-squared error minimization, are applied to devise algorithms with tractable computational complexity. Decentralized algorithms are proposed to enable JP even with limited backhaul connectivity. These algorithms rely provide a variety of alternatives for signaling overhead, computational complexity and convergence behavior. Time division duplexing is exploited to design transceiver training techniques for two scenarios: stream specific estimation and direct estimation. In the stream specific estimation, the base station and user equipment estimate all of the stream specific precoded pilots individually and construct the transmit/receive covariance matrices based on these pilot estimates. With the direct estimation, only the intended transmission is separately estimated and the covariance matrices constructed directly from the aggregate system-wide pilots. The impact of feedback/backhaul signaling quantization is considered, in order to further reduce the signaling overhead. Also, user admission is being considered for time-correlated channels. The enhanced transceiver convergence rate enables periodic beamformer reinitialization, which greatly improves the achieved system performance in dense networks.

Includes members:
  Member "distributed bi directional training of nonlinear precoders and receivers in cellular networks":
    also connected through other hyperedges:
      (none)
  Member "decomposition by partial linearization parallel optimization of multi agent systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "convergent policy optimization for safe reinforcement learning")
  Member "backhaul limited asymmetric cooperation for mimo cellular networks via semidefinite relaxation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cloud edge coordinated processing low latency multicasting transmission")
  Member "base station activation and linear transceiver design for optimal resource management in heterogeneous networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "power efficient resource allocation in c rans with sinr constraints and deadlines")
  Member "joint precoding and multivariate backhaul compression for the downlink of cloud radio access networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "caching at base stations with multi cluster multicast wireless backhaul via accelerated first order algorithm")
  Member "distributed robust multicell coordinated beamforming with imperfect csi an admm approach":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "robust urllc packet scheduling of ofdm systems")
  Member "adaptive spatial intercell interference cancellation in multicell wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "provisioning energy efficiency and qos for multi carrier comp with limited feedback")
  Member "pilot contamination and precoding in multi cell tdd systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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)