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: "causal network inference by optimal causation entropy"
  Abstract: The broad abundance of time series data, which is in sharp contrast to limited knowledge of the underlying network dynamic processes that produce such observations, calls for a rigorous and efficient method of causal network inference. Here we develop mathematical theory of causation entropy, an information-theoretic statistic designed for model-free causality inference. For stationary Markov processes, we prove that for a given node in the network, its causal parents form the minimal set of nodes that maximizes causation entropy, a result we refer to as the optimal causation entropy principle. Furthermore, this principle guides us in developing computational and data efficient algorithms for causal network inference based on a two-step discovery and removal algorithm for time series data for a network-coupled dynamical system. Validation in terms of analytical and numerical results for Gaussian processes on large random networks highlights that inference by our algorithm outperforms previous leading meth...

Connected through hyperedge HE-1 (induced by source paper "the design of global correlation quantifiers and continuous notions of statistical sufficiency"):
  with members:
    - "auto encoding total correlation explanation"

Connected through hyperedge HE-2 (induced by source paper "towards a framework for observational causality from time series when shannon meets turing"):
  with members:
    - "a survey of learning causality with data problems and methods"
    - "interaction information for causal inference the case of directed triangle"
    - "dynamic network models for forecasting"
    - "nonnegative decomposition of multivariate information"

Connected through hyperedge HE-3 (induced by source paper "connecting network science and information theory"):
  with members:
    - "knowledge acquisition a complex networks approach"
    - "using network science and text analytics to produce surveys in a scientific topic"
    - "probing the topological properties of complex networks modeling short written texts"
    - "comparing the topological properties of real and artificially generated scientific manuscripts"

Connected through hyperedge HE-4 (induced by source paper "deriving pairwise transfer entropy from network structure and motifs"):
  with members:
    - "idtxl the information dynamics toolkit xl a python package for the efficient analysis of multivariate information dynamics in networks"
    - "leveraging information storage to select forecast optimal parameters for delay coordinate reconstructions"
    - "control principles of complex networks"

Connected through hyperedge HE-5 (induced by source paper "potential conditional mutual information estimators properties and applications"):
  with members:
    - "ensemble estimation of mutual information"
    - "estimating mutual information for discrete continuous mixtures"
    - "causal strength via shannon capacity axioms estimators and applications"
    - "conditional dependence via shannon capacity axioms estimators and applications"
    - "learning temporal dependence from time series data with latent variables"
    - "minimax estimation of functionals of discrete distributions"
    - "distinguishing cause from effect using observational data methods and benchmarks"
    - "minimax rates of entropy estimation on large alphabets via best polynomial approximation"

Connected through hyperedge HE-6 (induced by source paper "budgeted experiment design for causal structure learning"):
  with members:
    - "counting and uniform sampling from markov equivalent dags"
    - "learning causal structures using regression invariance"
    - "cost optimal learning of causal graphs"
    - "learning causal graphs with small interventions"
    - "an algorithm for deciding if a set of observed independencies has a causal explanation"
    - "causal discovery from a mixture of experimental and observational data"
    - "directed information graphs"
    - "bayesian model averaging using the k best bayesian network structures"

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)