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: "non asymptotic equipartition properties for independent and identically distributed sources"
  Abstract: Given an independent and identically distributed source $X = \{X_i \}_{i=1}^{\infty}$ with finite Shannon entropy or differential entropy (as the case may be) $H(X)$, the non-asymptotic equipartition property (NEP) with respect to $H(X)$ is established, which characterizes, for any finite block length $n$, how close $-{1\over n} \ln p(X_1 X_2...X_n)$ is to $H(X)$ by determining the information spectrum of $X_1 X_2...X_n $, i.e., the distribution of $-{1\over n} \ln p(X_1 X_2...X_n)$. Non-asymptotic equipartition properties (with respect to conditional entropy, mutual information, and relative entropy) in a similar nature are also established. These non-asymptotic equipartition properties are instrumental to the development of non-asymptotic coding (including both source and channel coding) results in information theory in the same way as the asymptotic equipartition property to all asymptotic coding theorems established so far in information theory. As an example, the NEP with respect to $H(X)$ is used to establish a non-asymptotic fixed rate source coding theorem, which reveals, for any finite block length $n$, a complete picture about the tradeoff between the minimum rate of fixed rate coding of $X_1...X_n$ and error probability when the error probability is a constant, or goes to 0 with block length $n$ at a sub-polynomial, polynomial or sub-exponential speed. With the help of the NEP with respect to other information quantities, non-asymptotic channel coding theorems of similar nature will be established in a separate paper.

Connected through hyperedge HE-1 (induced by source paper "a randomized approach to the capacity of finite state channels"):
  with members:
    - "the bethe permanent of a nonnegative matrix"
    - "limit theorems in hidden markov models"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "new non asymptotic random channel coding theorems"):
  with members:
    - "moderate deviations in channel coding"
    - "jar decoding non asymptotic converse coding theorems taylor type expansion and optimality"
    - "information spectrum approach to second order coding rate in channel coding"

Connected through hyperedge HE-3 (induced by source paper "limit theorems in hidden markov models"):
  with members:
    - "entropy rate for hidden markov chains with rare transitions"
    - "the capacity of finite state channels in the high noise regime"
    - "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)