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: "entropy rate for hidden markov chains with rare transitions"
  Abstract: We consider Hidden Markov Chains obtained by passing a Markov Chain with rare transitions through a noisy memoryless channel. We obtain asymptotic estimates for the entropy of the resulting Hidden Markov Chain as the transition rate is reduced to zero. Let (Xn) be a Markov chain with finite state space S and transition matrix P(p) and let (Yn) be the Hidden Markov chain observed by passing (Xn) through a homogeneous noisy memoryless channel (i.e. Y takes values in a set T, and there exists a matrix Q such that P(Yn = jjXn = i;X n−1 −1 ;X 1+1;Y n−1 −1 ;Y 1 n+1) = Qij). We make the additional assumption on the channel that the rows of Q are distinct. In this case we call the channel statistically distinguishing. We assume that P(p) is of the form I + pA where A is a matrix with negative entries on the diagonal, non-negative entries in the off-diagonal terms and zero row sums. We further assume that for small positive p, the Markov chain with transition matrix P(p) is irreducible. Notice that for Markov chains of this form, the invariant distribution (�i)i2 S does not depend on p. In this case, we say that for small positive values of p, the Markov chain is in a rare transition regime. We will adopt the convention that H is used to denote the entropy of a fi- nite partition, whereas h is used to denote the entropy of a process (the en- tropy rate in information theory terminology). Given an irreducible Markov chain with transition matrix P, we let h(P) be the entropy of the Markov chain (i.e. h(P) = − P i;jiPij logPij wherei is the (unique) invariant distribution of the Markov chain and as usual we adopt the convention that 0log0 = 0). We also let Hchan(i) be the entropy of the output of the channel when the input symbol is i (i.e. Hchan(i) = − P j2 T Qij logQij). Let h(Y ) denote the entropy of Y (i.e.

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"
    - "the capacity of finite state channels in the high noise regime"
    - "derivatives of entropy rate in special families of hidden markov chains"

Connected through hyperedge HE-2 (induced by source paper "on the performance of short block codes over finite state channels in the rare transition regime"):
  with members:
    - "delay sensitive communication over fading channel queueing behavior and code parameter selection"
    - "stationary and transition probabilities in slow mixing long memory markov processes"

Connected through hyperedge HE-3 (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"
    - "limit theorems in hidden markov models"
    - "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-4 (induced by source paper "limit theorems in hidden markov models"):
  with members:
    - "non asymptotic equipartition properties for independent and identically distributed sources"
    - "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)