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 "limit theorems in hidden markov models"):
  Abstract: In this paper, under mild assumptions, we derive a law of large numbers, a central limit theorem with an error estimate, an almost sure invariance principle and a variant of Chernoff bound in finite-state hidden Markov models. These limit theorems are of interest in certain ares in statistics and information theory. Particularly, we apply the limit theorems to derive the rate of convergence of the maximum likelihood estimator in finite-state hidden Markov models.

Includes members:
  Member "non asymptotic equipartition properties for independent and identically distributed sources":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a randomized approach to the capacity of finite state channels")
  Member "entropy rate for hidden markov chains with rare transitions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "novel lower bounds on the entropy rate of binary hidden markov processes")
  Member "the capacity of finite state channels in the high noise regime":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "novel lower bounds on the entropy rate of binary hidden markov processes")
  Member "concavity of mutual information rate for input restricted finite state memoryless channels at high snr":
    also connected through other hyperedges:
      (none)
  Member "asymptotics of entropy rate in special families of hidden markov chains":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "feedback capacity and coding for the bibo channel with a no repeated ones input constraint")
  Member "derivatives of entropy rate in special families of hidden markov chains":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "analyticity of entropy rates of continuous state hidden markov models")
  Member "taylor series expansions for the entropy rate of hidden markov processes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a randomized approach to the capacity of finite state channels")
  Member "the entropy of a binary hidden markov process":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a randomized approach to the capacity of finite state channels")

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)