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: "consistency of the plug in estimator of the entropy rate for ergodic processes"
  Abstract: A plug-in estimator of entropy is the entropy of the distribution where probabilities of symbols or blocks have been replaced with their relative frequencies in the sample. Consistency and asymptotic unbiasedness of the plug-in estimator can be easily demonstrated in the IID case. In this paper, we ask whether the plug-in estimator can be used for consistent estimation of the entropy rate h of a stationary ergodic process. The answer is positive if, to estimate block entropy of order k, we use a sample longer than 2k(h+∊), whereas it is negative if we use a sample shorter than 2k(h−∊). In particular, if we do not know the entropy rate h, it is sufficient to use a sample of length (|X| + ∊)k where |X| is the alphabet size. The result is derived using k-block coding. As a by-product of our technique, we also show that the block entropy of a stationary process is bounded above by a nonlinear function of the average block entropy of its ergodic components. This inequality can be used for an alternative proof of the known fact that the entropy rate a stationary process equals the average entropy rate of its ergodic components.

Connected through hyperedge HE-1 (induced by source paper "maximal repetition and zero entropy rate"):
  with members:
    - "beyond word frequency bursts lulls and scaling in the temporal distributions of words"
    - "estimating the entropy of binary time series methodology some theory and a simulation study"

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)