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 "optimal multiuser zero forcing with per antenna power constraints for network mimo coordination"):
  Abstract: We consider a multicell multiple-input multiple-output (MIMO) coordinated downlink transmission, also known as network MIMO, under per-antenna power constraints. We investigate a simple multiuser zero-forcing (ZF) linear precoding technique known as block diagonalization (BD) for network MIMO. The optimal form of BD with per-antenna power constraints is proposed. It involves a novel approach of optimizing the precoding matrices over the entire null space of other users' transmissions. An iterative gradient descent method is derived by solving the dual of the throughput maximization problem, which finds the optimal precoding matrices globally and efficiently. The comprehensive simulations illustrate several network MIMO coordination advantages when the optimal BD scheme is used. Its achievable throughput is compared with the capacity region obtained through the recently established duality concept under per-antenna power constraints.

Includes members:
  Member "cooperative multi cell block diagonalization with per base station power constraints":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cooperative downlink interference transmission and cancellation for cellular connected uav a divide and conquer approach")
  Member "mimo broadcast channel optimization under general linear constraints":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dual link algorithm for the weighted sum rate maximization in mimo interference channels")
  Member "networked mimo with clustered linear precoding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimizing throughput performance in distributed mimo wi fi networks using deep reinforcement learning")

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)