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: "robust hypothesis testing with a relative entropy tolerance"
  Abstract: This paper considers the design of a minimax test for two hypotheses where the actual probability densities of the observations are located in neighborhoods obtained by placing a bound on the relative entropy between actual and nominal densities. The minimax problem admits a saddle point which is characterized. The robust test applies a nonlinear transformation which flattens the nominal likelihood ratio in the vicinity of one. Results are illustrated by considering the transmission of binary data in the presence of additive noise.

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

Connected through hyperedge HE-2 (induced by source paper "asymptotically minimax robust hypothesis testing"):
  with members:
    - "minimax robust decentralized detection in parallel sensor networks"
    - "old bands new tracks revisiting the band model for robust hypothesis testing"
    - "minimax robust hypothesis testing"

Connected through hyperedge HE-3 (induced by source paper "minimax robust decentralized detection in parallel sensor networks"):
  with members:
    - "robust decentralized detection and social learning in tandem networks"

Connected through hyperedge HE-4 (induced by source paper "sequentiality and adaptivity gains in active hypothesis testing"):
  with members:
    - "active sequential hypothesis testing"
    - "controlled sensing for multihypothesis testing"
    - "universal and composite hypothesis testing via mismatched divergence"
    - "non adaptive probabilistic group testing with noisy measurements near optimal bounds with efficient algorithms"
    - "active classification theory and application to underwater inspection"

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)