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: "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.

Connected through hyperedge HE-1 (induced by source paper "on continuous time gaussian channels"):
  with members:
    - "upper and lower bounds on the capacity of amplitude constrained mimo channels"
    - "recent results in continuous time network information theory"
    - "wireless network information flow a deterministic approach"
    - "directed information causal estimation and communication in continuous time"
    - "feedback capacity of the gaussian interference channel to within 2 bits"
    - "on the gaussian mac with imperfect feedback"
    - "a new outer bound and the noisy interference sum rate capacity for gaussian interference channels"
    - "capacity bounds for the gaussian interference channel"

Connected through hyperedge HE-2 (induced by source paper "a randomized approach to the capacity of finite state channels"):
  with members:
    - "the bethe permanent of a nonnegative matrix"
    - "non asymptotic equipartition properties for independent and identically distributed sources"
    - "entropy rate for hidden markov chains with rare transitions"
    - "entropy of hidden markov processes via cycle expansion"
    - "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)