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 outlying sequence detection for continuous observations"
  Abstract: The following detection problem is studied, in which there are $M$ sequences of samples out of which one outlier sequence needs to be detected. Each typical sequence contains $n$ independent and identically distributed (i.i.d.) continuous observations from a known distribution $\pi$, and the outlier sequence contains $n$ i.i.d. observations from an outlier distribution $\mu$, which is distinct from $\pi$, but otherwise unknown. A universal test based on KL divergence is built to approximate the maximum likelihood test, with known $\pi$ and unknown $\mu$. A data-dependent partitions based KL divergence estimator is employed. Such a KL divergence estimator is further shown to converge to its true value exponentially fast when the density ratio satisfies $0<K_1\leq \frac{d\mu}{d\pi}\leq K_2$, where $K_1$ and $K_2$ are positive constants, and this further implies that the test is exponentially consistent. The performance of the test is compared with that of a recently introduced test for this problem based on the machine learning approach of maximum mean discrepancy (MMD). We identify regimes in which the KL divergence based test is better than the MMD based test.

Connected through hyperedge HE-1 (induced by source paper "minimax optimal estimation of kl divergence for continuous distributions"):
  with members:
    - "estimation of kl divergence optimal minimax rate"
    - "minimax rate optimal estimation of divergences between discrete distributions"
    - "demystifying fixed k nearest neighbor information estimators"
    - "ensemble estimation of multivariate f divergence"
    - "minimax rates of entropy estimation on large alphabets via best polynomial approximation"

Connected through hyperedge HE-2 (induced by source paper "linear complexity exponentially consistent tests for universal outlying sequence detection"):
  with members:
    - "nonparametric detection of anomalous data streams"
    - "universal outlier hypothesis testing"
    - "nonparametric divergence estimation with applications to machine learning on distributions"

Connected through hyperedge HE-3 (induced by source paper "estimation of kl divergence optimal minimax rate"):
  with members:
    - "inhomogoenous hypergraph clustering with applications"
    - "minimax estimation of kl divergence between discrete distributions"
    - "minimax rate optimal estimation of divergences between discrete distributions"
    - "ensemble estimation of multivariate f divergence"
    - "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"
    - "renyi divergence and kullback leibler divergence"

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)