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: "dual capacity upper bounds for noisy runlength constrained channels"
  Abstract: Binary-input memoryless channels with a runlength constrained input, where an input of one is necessarily followed by a fixed number of zeros, are considered. Computable upper bounds to the capacity of such noisy runlength constrained channels are derived using the dual capacity method. Simplified versions of the bounds are presented for the binary erasure channel (BEC) and the binary symmetric channel (BSC). These bounds improve upon previously known computable bounds and show that feedback strictly improves the capacity of the runlength constrained BEC and BSC for all parameters.

Connected through hyperedge HE-1 (induced by source paper "computable lower bounds for capacities of input driven finite state channels"):
  with members:
    - "feedback capacity and coding for the bibo channel with a no repeated ones input constraint"
    - "asymptotics of input constrained erasure channel capacity"
    - "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-2 (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"
    - "feedback capacity and coding for the bibo channel with a no repeated ones input constraint"
    - "an upper bound for the capacity of amplitude constrained scalar awgn channel"
    - "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-3 (induced by source paper "feedback capacity and coding for the bibo channel with a no repeated ones input constraint"):
  with members:
    - "asymptotics of input constrained erasure channel capacity"
    - "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"
    - "can feedback increase the capacity of the energy harvesting channel"
    - "extrinsic jensen shannon divergence applications to variable length coding"
    - "capacity and coding for the ising channel with feedback"
    - "optimal feedback communication via posterior matching"

Connected through hyperedge HE-4 (induced by source paper "asymptotics of input constrained erasure channel capacity"):
  with members:
    - "the feedback capacity of the 1 rll input constrained erasure channel"
    - "asymptotics of entropy rate in special families of hidden markov chains"
    - "derivatives of entropy rate in special families of hidden markov chains"
    - "the capacity of channels with feedback"

Connected through hyperedge HE-5 (induced by source paper "capacity bounds for discrete time amplitude constrained additive white gaussian noise channels"):
  with members:
    - "an upper bound for the capacity of amplitude constrained scalar awgn channel"
    - "peak to average power ratio of good codes for gaussian channel"
    - "on the monotonicity log concavity and tight bounds of the generalized marcum and nuttall q functions"
    - "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)