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 "feedback capacity and coding for the bibo channel with a no repeated ones input constraint"):
  Abstract: In this paper, a general binary-input binary-output (BIBO) channel is investigated in the presence of feedback and input constraints. The feedback capacity and the optimal input distribution of this setting are calculated for the case of an $(1,\infty)$-RLL input constraint, that is, the input sequence contains no consecutive ones. These results are obtained via explicit solution of a corresponding dynamic programming optimization problem. A simple coding scheme is designed based on the principle of posterior matching, which was introduced by Shayevitz and Feder for memoryless channels. The posterior matching scheme for our input-constrained setting is shown to achieve capacity using two new ideas: \textit{message history}, which captures the memory embedded in the setting, and \textit{message splitting}, which eases the analysis of the scheme. Additionally, in the special case of an S-channel, we give a very simple zero-error coding scheme that is shown to achieve capacity. For the input-constrained BSC, we show using our capacity formula that feedback increases capacity when the cross-over probability is small.

Includes members:
  Member "asymptotics of input constrained erasure channel capacity":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "computable lower bounds for capacities of input driven finite state channels")
  Member "dual capacity upper bounds for noisy runlength constrained channels":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "computable lower bounds for capacities of input driven finite state channels")
  Member "a single letter upper bound on the feedback capacity of unifilar finite state channels":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "computing the feedback capacity of finite state channels using reinforcement learning")
  Member "an efficient feedback coding scheme with low error probability for discrete memoryless channels":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "active learning and csi acquisition for mmwave initial alignment")
  Member "a simple proof for the optimality of randomized posterior matching":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "active learning and csi acquisition for mmwave initial alignment")
  Member "can feedback increase the capacity of the energy harvesting channel":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "capacity of burst noise erasure channels with and without feedback and input cost")
  Member "extrinsic jensen shannon divergence applications to variable length coding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "low complexity algorithms for transmission of short blocks over the bsc with full feedback")
  Member "capacity and coding for the ising channel with feedback":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)