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: "strong converses for group testing in the finite blocklength regime"
  Abstract: We prove new strong converse results in a variety of group testing settings, generalizing a result of Baldassini, Johnson and Aldridge. These results are proved by two distinct approaches, corresponding to the non-adaptive and adaptive cases. In the non-adaptive case, we mimic the hypothesis testing argument introduced in the finite blocklength channel coding regime by Polyanskiy, Poor and Verd\'{u}. In the adaptive case, we combine a formulation based on directed information theory with ideas of Kemperman, Kesten and Wolfowitz from the problem of channel coding with feedback. In both cases, we prove results which are valid for finite sized problems, and imply capacity results in the asymptotic regime. These results are illustrated graphically for a range of models.

Connected through hyperedge HE-1 (induced by source paper "secure group testing"):
  with members:
    - "the capacity of bernoulli nonadaptive group testing"
    - "converse bounds for noisy group testing with arbitrary measurement matrices"
    - "limits on support recovery with probabilistic models an information theoretic framework"
    - "partition information and its transmission over boolean multi access channels"
    - "information theoretic bounds for adaptive sparse recovery"
    - "the capacity of non identical adaptive group testing"
    - "group testing algorithms bounds and simulations"
    - "the capacity of adaptive group testing"

Connected through hyperedge HE-2 (induced by source paper "improved group testing rates with constant column weight designs"):
  with members:
    - "the capacity of bernoulli nonadaptive group testing"
    - "limits on support recovery with probabilistic models an information theoretic framework"
    - "group testing algorithms bounds and simulations"
    - "the capacity of adaptive group testing"
    - "boolean compressed sensing and noisy group testing"
    - "non adaptive probabilistic group testing with noisy measurements near optimal bounds with efficient algorithms"

Connected through hyperedge HE-3 (induced by source paper "converse bounds for noisy group testing with arbitrary measurement matrices"):
  with members:
    - "the capacity of bernoulli nonadaptive group testing"
    - "asymptotics of fingerprinting and group testing tight bounds from channel capacities"
    - "limits on support recovery with probabilistic models an information theoretic framework"
    - "the capacity of adaptive group testing"
    - "boolean compressed sensing and noisy group testing"
    - "unbounded contention resolution in multiple access channels"

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)