human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "computing the feedback capacity of finite state channels using reinforcement learning"):
  with members:
    - "deepcode feedback codes via deep learning"
    - "a single letter upper bound on the feedback capacity of unifilar finite state channels"
    - "continuous control with deep reinforcement learning"
    - "capacity of a post channel with and without feedback"
    - "capacity and coding for the ising channel with feedback"
    - "finite state channels with time invariant deterministic feedback"
    - "the capacity of channels with feedback"

Connected through hyperedge HE-2 (induced by source paper "computable lower bounds for capacities of input driven finite state channels"):
  with members:
    - "asymptotics of input constrained erasure channel capacity"
    - "dual capacity upper bounds for noisy runlength constrained channels"
    - "novel lower bounds on the entropy rate of binary hidden markov processes"
    - "a single letter upper bound on the feedback capacity of unifilar finite state channels"

Connected through hyperedge HE-3 (induced by source paper "computable upper bounds on the capacity of finite state channels"):
  with members:
    - "capacity of burst noise erasure channels with and without feedback and input cost"
    - "an upper bound for the capacity of amplitude constrained scalar awgn channel"
    - "dual capacity upper bounds for noisy runlength constrained channels"
    - "capacity bounds for discrete time amplitude constrained additive white gaussian noise channels"
    - "capacity of a post channel with and without feedback"
    - "capacity and coding for the ising channel with feedback"
    - "the capacity of channels with feedback"

Connected through hyperedge HE-4 (induced by source paper "graph based encoders and their performance for finite state channels with feedback"):
  with members:
    - "feedback capacity and coding for the 0 k rll input constrained bec"
    - "a single letter upper bound on the feedback capacity of unifilar finite state channels"
    - "a comprehensive survey of recent advancements in molecular communication"
    - "capacity and coding for the ising channel with feedback"
    - "write channel model for bit patterned media recording"
    - "optimal feedback communication via posterior matching"
    - "the capacity of channels with feedback"

Connected through hyperedge HE-5 (induced by source paper "capacity of burst noise erasure channels with and without feedback and input cost"):
  with members:
    - "asymptotics of input constrained erasure channel capacity"
    - "feedback does not increase the capacity of compound channels with additive noise"
    - "can feedback increase the capacity of the energy harvesting channel"
    - "zero delay sequential transmission of markov sources over burst erasure channels"
    - "windowed decoding of protograph based ldpc convolutional codes over erasure channels"
    - "capacity and coding for the ising channel with feedback"
    - "a coding theorem for a class of stationary channels with feedback"
    - "finite state channels with time invariant deterministic feedback"

Connected through hyperedge HE-6 (induced by source paper "feedback capacity and coding for the 0 k rll input constrained bec"):
  with members:
    - "a single letter upper bound on the feedback capacity of unifilar finite state channels"
    - "an efficient feedback coding scheme with low error probability for discrete memoryless channels"
    - "a simple proof for the optimality of randomized posterior matching"
    - "extrinsic jensen shannon divergence applications to variable length coding"
    - "modulation coding for flash memories"
    - "capacity and coding for the ising channel with feedback"
    - "optimal feedback communication via posterior matching"
    - "the capacity of channels with feedback"

Connected through hyperedge HE-7 (induced by source paper "optimal zero delay coding of markov sources stationary and finite memory codes"):
  with members:
    - "zero delay sequential transmission of markov sources over burst erasure channels"
    - "finite state channels with time invariant deterministic feedback"
    - "the capacity of channels with feedback"

Connected through hyperedge HE-8 (induced by source paper "dual capacity upper bounds for noisy runlength constrained channels"):
  with members:
    - "on the capacity of free space optical intensity channels"

Question: Please tell me which class the center node 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)