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 "learning in an uncertain world mimo covariance matrix optimization with imperfect feedback"):
  Abstract: In this paper, we present a distributed learning algorithm for the optimization of signal covariance matrices in Gaussian multiple–input and multiple–output (MIMO) multiple access channel with imperfect (and possibly delayed) feedback. The algorithm is based on the method of matrix exponential learning (MXL) and it has the same information and computation requirements as distributed water-filling. However, unlike water-filling, the proposed algorithm converges to the system’s optimum signal covariance profile even under stochastic uncertainty and imperfect feedback. Moreover, the algorithm also retains its convergence properties in the presence of user update asynchronicities, random delays and/or ergodically changing channel conditions. Our theoretical analysis is complemented by extensive numerical simulations which illustrate the robustness and scalability of MXL in realistic network conditions. In particular, the algorithm retains its convergence speed even for large numbers of users and/or antennas per user.

Includes members:
  Member "a parallel decomposition method for nonconvex stochastic multi agent optimization problems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "convergent policy optimization for safe reinforcement learning")
  Member "massive mimo for next generation wireless systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "joint power control and lsfd for wireless powered cell free massive mimo")
  Member "what will 5g be":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "5g network slicing using sdn and nfv a survey of taxonomy architectures and future challenges")
  Member "transmit without regrets online optimization in mimo ofdm cognitive radio systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a learning approach for low complexity optimization of energy efficiency in multicarrier wireless networks")
  Member "scaling up mimo opportunities and challenges with very large arrays":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "downlink secrecy rate of one bit massive mimo system with active eavesdropping")
  Member "distributed learning policies for power allocation in multiple access channels":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "low complexity distributed radio access network slicing algorithms and experimental results")
  Member "regularization techniques for learning with matrices":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "distributed learning with sublinear communication")
  Member "the mimo iterative waterfilling algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "distributed power control with partial channel state information performance characterization and design")

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)