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 "capacity achieving distributions information lossless randomized strategies for feedback channels with memory the lqg theory of directed information part ii"):
  Abstract: A methodology is developed to realized optimal channel input conditional distributions, which maximize the finite-time horizon directed information, for channels with memory and feedback, by information lossless randomized strategies. The methodology is applied to general Time-Varying Multiple Input Multiple Output (MIMO) Gaussian Linear Channel Models (G-LCMs) with memory, subject to average transmission cost constraints of quadratic form. The realizations of optimal distributions by randomized strategies are shown to exhibit a decomposion into a deterministic part and a random part. The decomposition reveals the dual role of randomized strategies, to control the channel output process and to transmit new information over the channels. Moreover, a separation principle is shown between the computation of the optimal deterministic part and the random part of the randomized strategies. The dual role of randomized strategies generalizes the Linear-Quadratic-Gaussian (LQG) stochastic optimal control theory to directed information pay-offs. The characterizations of feedback capacity are obtained from the per unit time limits of finite-time horizon directed information, without imposing \'a priori assumptions, such as, stability of channel models or ergodicity of channel input and output processes. For time-invariant MIMO G-LCMs with memory, it is shown that whether feedback increases capacity, is directly related to the channel parameters and the transmission cost function, through the solutions of Riccati matrix equations, and moreover for unstable channels, feedback capacity is non-zero, provided the power exceeds a critical level.

Includes members:
  Member "information structures of capacity achieving distributions for feedback channels with memory and transmission cost stochastic optimal control variational equalities part i":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a connection between feedback capacity and kalman filter for colored gaussian noises")
  Member "capacity of binary state symmetric channel with and without feedback and transmission cost":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "single letter expression of capacity for a class of channels with memory")
  Member "capacity of a post channel with and without feedback":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "computing the feedback capacity of finite state channels using reinforcement learning")
  Member "a coding theorem for a class of stationary channels with feedback":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "importance of small probability events in big data information measures applications and challenges")
  Member "finite state channels with time invariant deterministic feedback":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "computing the feedback capacity of finite state channels using reinforcement learning")
  Member "feedback capacity of stationary gaussian channels":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "capacity of continuous channels with memory via directed information neural estimator")
  Member "the capacity of channels with feedback":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a connection between feedback capacity and kalman filter for colored gaussian noises")

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)