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: "estimating the entropy of binary time series methodology some theory and a simulation study"
  Abstract: Abstract: Partly motivated by entropy-estimation problems in neuroscience, we present adetailed and extensive comparison between some of the most popular and effective entropyestimation methods used in practice: The plug-in method, four different estimators basedon the Lempel-Ziv (LZ) family of data compression algorithms, an estimator based on theContext-Tree Weighting (CTW) method, and the renewal entropy estimator.M ETHODOLOGY : Three new entropy estimators are introduced; two new LZ-basedestimators, and the “renewal entropy estimator,” which is tailored to data generated by abinary renewal process. For two of the four LZ-based estimators, a bootstrap procedure isdescribed for evaluating their standard error, and a practical rule of thumb is heuristicallyderived for selecting the values of their parameters in practice. T HEORY : We prove that,unlike their earlier versions, the two new LZ-based estimators are universally consistent,that is, they converge to the entropy rate for every ﬁnite-valued, stationary and ergodicprocess. An effective method is derived for the accurate approximation of the entropy rateof a ﬁnite-state hidden Markov model (HMM) with known distribution. Heuristiccalculations are presented and approximate formulas are derived for evaluating the bias andthe standard error of each estimator. S

Connected through hyperedge HE-1 (induced by source paper "on a class of markov order estimators based on ppm and other universal codes"):
  with members:
    - "scaling laws for neural language models"
    - "calibration entropy rates and memory in language models"
    - "regular hilberg processes an example of processes with a vanishing entropy rate"
    - "mixing ergodic and nonergodic processes with rapidly growing information between blocks"

Connected through hyperedge HE-2 (induced by source paper "practical prediction of human movements across device types and spatiotemporal granularities"):
  with members:
    - "learning the relation between mobile encounters and web traffic patterns a data driven study"
    - "deep learning in neural networks"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-3 (induced by source paper "the word entropy of natural languages"):
  with members:
    - "complexity and universality in the long range order of words"
    - "a universal part of speech tagset"

Connected through hyperedge HE-4 (induced by source paper "maximal repetition and zero entropy rate"):
  with members:
    - "consistency of the plug in estimator of the entropy rate for ergodic processes"
    - "beyond word frequency bursts lulls and scaling in the temporal distributions of words"

Connected through hyperedge HE-5 (induced by source paper "complexity and universality in the long range order of words"):
  with members:
    - "beyond word frequency bursts lulls and scaling in the temporal distributions of words"
    - "statistical keyword detection in literary corpora"

Connected through hyperedge HE-6 (induced by source paper "does dirichlet prior smoothing solve the shannon entropy estimation problem"):
  with members:
    - "estimation of kl divergence optimal minimax rate"
    - "minimax rate optimal estimation of divergences between discrete distributions"
    - "adaptive estimation of shannon entropy"
    - "minimax estimation of functionals of discrete distributions"
    - "maximum likelihood estimation of functionals of discrete distributions"
    - "minimax rates of entropy estimation on large alphabets via best polynomial approximation"

Connected through hyperedge HE-7 (induced by source paper "statistical patterns in written language"):
  with members:
    - "statistical keyword detection in literary corpora"
    - "zipf s law and the creation of musical context"

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)