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: "the capacity of finite state channels in the high noise regime"
  Abstract: This paper considers the derivative of the entropy rate of a hidden Markov process with respect to the observation probabilities. The main result is a compact formula for the derivative that can be evaluated easily using Monte Carlo methods. It is applied to the problem of computing the capacity of a finite-state channel (FSC) and, in the high-noise regime, the formula has a simple closed-form expression that enables series expansion of the capacity of a FSC. This expansion is evaluated for a binary-symmetric channel under a (0,1) run-length limited constraint and an intersymbol-interference channel with Gaussian noise.

Connected through hyperedge HE-1 (induced by source paper "novel lower bounds on the entropy rate of binary hidden markov processes"):
  with members:
    - "strong data processing inequalities for channels and bayesian networks"
    - "on the entropy of a noisy function"
    - "minimum ms e gerber s lemma"
    - "entropy rate for hidden markov chains with rare transitions"
    - "derivatives of entropy rate in special families of hidden markov chains"

Connected through hyperedge HE-2 (induced by source paper "limit theorems in hidden markov models"):
  with members:
    - "non asymptotic equipartition properties for independent and identically distributed sources"
    - "entropy rate for hidden markov chains with rare transitions"
    - "concavity of mutual information rate for input restricted finite state memoryless channels at high snr"
    - "asymptotics of entropy rate in special families of hidden markov chains"
    - "derivatives of entropy rate in special families of hidden markov chains"
    - "taylor series expansions for the entropy rate of hidden markov processes"
    - "the entropy of a binary hidden markov process"

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)