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: "novel lower bounds on the entropy rate of binary hidden markov processes"
  Abstract: Recently, Samorodnitsky proved a strengthened version of Mrs. Gerber’s Lemma, where the output entropy of a binary symmetric channel is bounded in terms of the average entropy of the input projected on a random subset of coordinates. Here, this result is applied for deriving novel lower bounds on the entropy rate of binary hidden Markov processes. For symmetric underlying Markov processes, our bound improves upon the best known bound in the very noisy regime. The nonsymmetric case is also considered, and explicit bounds are derived for Markov processes that satisfy the (1,∞)-RLL constraint.

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"
    - "dual capacity upper bounds for noisy runlength constrained channels"
    - "a single letter upper bound on the feedback capacity of unifilar finite state channels"

Connected through hyperedge HE-2 (induced by source paper "strong data processing inequalities for channels and bayesian networks"):
  with members:
    - "converses for distributed estimation via strong data processing inequalities"
    - "on the entropy of a noisy function"
    - "strong data processing inequalities for input constrained additive noise channels"
    - "dissipation of information in channels with input constraints"
    - "strong data processing inequalities and phi sobolev inequalities for discrete channels"
    - "tractable inference for complex stochastic processes"
    - "on maximal correlation hypercontractivity and the data processing inequality studied by erkip and cover"
    - "on pairs of f divergences and their joint range"

Connected through hyperedge HE-3 (induced by source paper "a note on the strong data processing inequalities in bayesian networks"):
  with members:
    - "converses for distributed estimation via strong data processing inequalities"
    - "bounds between contraction coefficients"
    - "on the entropy of a noisy function"
    - "secret key generation with one communicator and a one shot converse via hypercontractivity"
    - "strong data processing inequalities for input constrained additive noise channels"
    - "dissipation of information in channels with input constraints"
    - "strong data processing inequalities and phi sobolev inequalities for discrete channels"
    - "tractable inference for complex stochastic processes"

Connected through hyperedge HE-4 (induced by source paper "on the entropy of a noisy function"):
  with members:
    - "strong data processing inequalities for channels and bayesian networks"
    - "an improved upper bound for the most informative boolean function conjecture"
    - "dissipation of information in channels with input constraints"
    - "analysis of boolean functions"

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)