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: "universal codes as a basis for time series testing"
  Abstract: We suggest a new approach to hypothesis testing for ergodic and stationary processes. In contrast to standard methods, the suggested approach gives a possibility to make tests, based on any lossless data compression method even if the distribution law of the codeword lengths is not known. We apply this approach to the following four problems: goodness-of-fit testing (or identity testing), testing for independence, testing of serial independence and homogeneity testing and suggest nonparametric statistical tests for these problems. It is important to note that practically used so-called archivers can be used for suggested testing.

Connected through hyperedge HE-1 (induced by source paper "independence clustering without a matrix"):
  with members:
    - "robust hierarchical clustering"
    - "kernel based conditional independence test and application in causal discovery"
    - "nonparametric statistical inference for ergodic processes"

Connected through hyperedge HE-2 (induced by source paper "hypotheses testing on infinite random graphs"):
  with members:
    - "independence clustering without a matrix"
    - "a universal grammar based code for lossless compression of binary trees"
    - "application of kolmogorov complexity and universal codes to identity testing and nonparametric testing of serial independence for time series"

Connected through hyperedge HE-3 (induced by source paper "unsupervised model free representation learning"):
  with members:
    - "optimal regret bounds for selecting the state representation in reinforcement learning"
    - "selecting the state representation in reinforcement learning"
    - "methods for computing state similarity in markov decision processes"
    - "consistency of feature markov processes"
    - "feature reinforcement learning part i unstructured mdps"
    - "pattern recognition for conditionally independent data"
    - "using information theory approach to randomness testing"

Connected through hyperedge HE-4 (induced by source paper "confidence sets in time series filtering"):
  with members:
    - "mdl denoising revisited"
    - "nonparametric statistical inference for ergodic processes"

Connected through hyperedge HE-5 (induced by source paper "on finding predictors for arbitrary families of processes"):
  with members:
    - "characterizing predictable classes of processes"
    - "on the possibility of learning in reactive environments with arbitrary dependence"
    - "on sequence prediction for arbitrary measures"

Connected through hyperedge HE-6 (induced by source paper "nonparametric statistical inference for ergodic processes"):
  with members:
    - "using data compressors to construct rank tests"
    - "application of kolmogorov complexity and universal codes to identity testing and nonparametric testing of serial independence for time series"
    - "universal codes as a basis for nonparametric testing of serial independence for time series"

Connected through hyperedge HE-7 (induced by source paper "using data compressors to construct rank tests"):
  with members:
    - "using information theory approach to randomness testing"
    - "the similarity metric"

Connected through hyperedge HE-8 (induced by source paper "compression based methods for nonparametric density estimation on line prediction regression and classification for time series"):
  with members:
    - "using information theory approach to randomness testing"

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)