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 outlier hypothesis testing"
  Abstract: Outlier hypothesis testing is studied in a universal setting. Multiple sequences of observations are collected, a small subset of which are outliers. A sequence is considered an outlier if the observations in that sequence are distributed according to an ``outlier'' distribution, distinct from the ``typical'' distribution governing the observations in all the other sequences. Nothing is known about the outlier and typical distributions except that they are distinct and have full supports. The goal is to design a universal test to best discern the outlier sequence(s). It is shown that the generalized likelihood test is universally exponentially consistent under various settings. The achievable error exponent is also characterized. In the other settings, it is also shown that there cannot exist any universally exponentially consistent test.

Connected through hyperedge HE-1 (induced by source paper "error exponents of mismatched likelihood ratio testing"):
  with members:
    - "universal and composite hypothesis testing via mismatched divergence"
    - "robust hypothesis testing with a relative entropy tolerance"

Connected through hyperedge HE-2 (induced by source paper "importance of small probability events in big data information measures applications and challenges"):
  with members:
    - "local deep feature alignment for unsupervised dimension reduction"
    - "matching users preference under target revenue constraints in data recommendation systems"
    - "differential message importance measure a new approach to the required sampling number in big data structure characterization"
    - "decorrelation of neutral vector variables theory and applications"
    - "group cooperation with optimal resource allocation in wireless powered communication networks"
    - "amplifying inter message distance on information divergence measures in big data"
    - "focusing on a probability element parameter selection of message importance measure in big data"
    - "linear complexity exponentially consistent tests for universal outlying sequence detection"

Connected through hyperedge HE-3 (induced by source paper "asymptotically optimal one and two sample testing with kernels"):
  with members:
    - "robust kullback leibler divergence and universal hypothesis testing for continuous distributions"
    - "mmd gan towards deeper understanding of moment matching network"
    - "m statistic for kernel change point detection"
    - "nonparametric detection of anomalous data streams"
    - "renyi divergence and kullback leibler divergence"
    - "universal and composite hypothesis testing via mismatched divergence"

Connected through hyperedge HE-4 (induced by source paper "k medoids clustering of data sequences with composite distributions"):
  with members:
    - "nonparametric composite hypothesis testing in an asymptotic regime"
    - "nonparametric detection of anomalous data streams"

Connected through hyperedge HE-5 (induced by source paper "hypothesis testing over the two hop relay network"):
  with members:
    - "distributed hypothesis testing with concurrent detections"
    - "strong converse for hypothesis testing against independence over a two hop network"
    - "distributed hypothesis testing based on unequal error protection codes"
    - "distributed hypothesis testing over discrete memoryless channels"
    - "on hypothesis testing against independence with multiple decision centers"
    - "heegard berger and cascade source coding problems with common reconstruction constraints"
    - "cascade and triangular source coding with side information at the first two nodes"
    - "cascade triangular and two way source coding with degraded side information at the second user"

Connected through hyperedge HE-6 (induced by source paper "linear complexity exponentially consistent tests for universal outlying sequence detection"):
  with members:
    - "universal outlying sequence detection for continuous observations"
    - "nonparametric detection of anomalous data streams"
    - "nonparametric divergence estimation with applications to machine learning on distributions"

Connected through hyperedge HE-7 (induced by source paper "learning to detect an oddball target"):
  with members:
    - "neural dissimilarity indices that predict oddball detection in behaviour"
    - "asymptotically optimal anomaly detection via sequential testing"
    - "active sequential hypothesis testing"
    - "sequentiality and adaptivity gains in active hypothesis testing"
    - "controlled sensing for multihypothesis testing"

Connected through hyperedge HE-8 (induced by source paper "asymptotically optimal anomaly detection via sequential testing"):
  with members:
    - "optimal index policies for anomaly localization in resource constrained cyber systems"
    - "active hypothesis testing for quickest anomaly detection"
    - "quickest search over multiple sequences with mixed observations"
    - "active sequential hypothesis testing"
    - "sequentiality and adaptivity gains in active hypothesis testing"
    - "controlled sensing for multihypothesis testing"
    - "sequential testing for sparse recovery"

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)