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 "dynamic transmit covariance design in mimo fading systems with unknown channel distributions and inaccurate channel state information"):
  Abstract: This paper considers dynamic transmit covariance design in point-to-point MIMO fading systems with unknown channel state distributions and inaccurate channel state information subject to both long term and short term power constraints. First, the case of instantaneous but possibly inaccurate channel state information at the transmitter (CSIT) is treated. By extending the drift-plus-penalty technique, a dynamic transmit covariance policy is developed and is shown to approach optimality with an $O(\delta)$ gap, where $\delta$ is the inaccuracy measure of CSIT, regardless of the channel state distribution and without requiring knowledge of this distribution. Next, the case of delayed and inaccurate channel state information is considered. The optimal transmit covariance solution that maximizes the ergodic capacity is fundamentally different in this case, and a different online algorithm based on convex projections is developed. The proposed algorithm for this delayed-CSIT case also has an $O(\delta)$ optimality gap, where $\delta$ is again the inaccuracy measure of CSIT.

Includes members:
  Member "learning in an uncertain world mimo covariance matrix optimization with imperfect feedback":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "online convex optimization and no regret learning algorithms guarantees and applications")
  Member "adaptive power allocation and control in time varying multi carrier mimo networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distributed stochastic optimization via matrix exponential learning")
  Member "learning to be green robust energy efficiency maximization in dynamic mimo ofdm systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a learning approach for low complexity optimization of energy efficiency in multicarrier wireless networks")
  Member "capacity achieving rateless polar codes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "circular buffer rate matched polar codes")
  Member "rateless coding for gaussian channels":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a framework for rate efficient control of distributed discrete systems")

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)