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: "using information theory approach to randomness testing"
  Abstract: We address the problem of detecting deviations of binary sequence from randomness,which is very important for random number (RNG) and pseudorandom number generators (PRNG). Namely, we consider a null hypothesis $H_0$ that a given bit sequence is generated by Bernoulli source with equal probabilities of 0 and 1 and the alternative hypothesis $H_1$ that the sequence is generated by a stationary and ergodic source which differs from the source under $H_0$. We show that data compression methods can be used as a basis for such testing and describe two new tests for randomness, which are based on ideas of universal coding. Known statistical tests and suggested ones are applied for testing PRNGs. Those experiments show that the power of the new tests is greater than of many known algorithms.

Connected through hyperedge HE-1 (induced by source paper "parallel decompression of gzip compressed files and random access to dna sequences"):
  with members:
    - "massively parallel lossless data decompression"

Connected through hyperedge HE-2 (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"
    - "universal codes as a basis for time series testing"
    - "pattern recognition for conditionally independent data"

Connected through hyperedge HE-3 (induced by source paper "experimental investigation of forecasting methods based on universal measures"):
  with members:
    - "compression based methods for nonparametric density estimation on line prediction regression and classification for time series"

Connected through hyperedge HE-4 (induced by source paper "applications of universal source coding to statistical analysis of time series"):
  with members:
    - "application of kolmogorov complexity and universal codes to identity testing and nonparametric testing of serial independence for time series"
    - "fast codes for large alphabets"

Connected through hyperedge HE-5 (induced by source paper "using data compressors to construct rank tests"):
  with members:
    - "universal codes as a basis for time series testing"
    - "the similarity metric"

Connected through hyperedge HE-6 (induced by source paper "compression based methods for nonparametric density estimation on line prediction regression and classification for time series"):
  with members:
    - "universal codes as a basis for time series testing"

Connected through hyperedge HE-7 (induced by source paper "universal codes as a basis for time series testing"):
  with members:
    - "universal codes as a basis for nonparametric testing of serial independence for time series"

Connected through hyperedge HE-8 (induced by source paper "application of kolmogorov complexity and universal codes to identity testing and nonparametric testing of serial independence for time series"):
  with members:
    - "universal codes as a basis for time series testing"
    - "universal codes as a basis for nonparametric testing of serial independence for time series"
    - "the similarity metric"
    - "minimum description length induction bayesianism and kolmogorov complexity"

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)