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 "single letter expression of capacity for a class of channels with memory"):
  Abstract: We study finite alphabet channels with Unit Memory on the previous Channel Outputs called UMCO channels. We identify necessary and sufficient conditions, to test whether the capacity achieving channel input distributions with feedback are time-invariant, and whether feedback capacity is characterized by single letter, expressions, similar to that of memoryless channels. The method is based on showing that a certain dynamic programming equation, which in general, is a nested optimization problem over the sequence of channel input distributions, reduces to a non-nested optimization problem. Moreover, for UMCO channels, we give a simple expression for the ML error exponent, and we identify sufficient conditions to test whether feedback does not increase capacity. We derive similar results, when transmission cost constraints are imposed. We apply the results to a special class of the UMCO channels, the Binary State Symmetric Channel (BSSC) with and without transmission cost constraints, to show that the optimization problem of feedback capacity is non-nested, the capacity achieving channel input distribution and the corresponding channel output transition probability distribution are time-invariant, and feedback capacity is characterized by a single letter formulae, precisely as Shannon's single letter characterization of capacity of memoryless channels. Then we derive closed form expressions for the capacity achieving channel input distribution and feedback capacity. We use the closed form expressions to evaluate an error exponent for ML decoding.

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 "sequential necessary and sufficient conditions for capacity achieving distributions of channels with memory and feedback")
  Member "information structures for feedback capacity of channels with memory and transmission cost stochastic optimal control variational equalities part i":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sequential necessary and sufficient conditions for capacity achieving distributions of channels with memory and feedback")
  Member "capacity of a post channel with and without feedback":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "computing the feedback capacity of finite state channels using reinforcement learning")
  Member "directed information on abstract spaces properties and variational equalities":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a converse bound characterization on zero delay indirect rdf for vector gauss markov sources")
  Member "capacity and coding for the ising channel with feedback":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "computing the feedback capacity of finite state channels using 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)