human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "limits on support recovery with probabilistic models an information theoretic framework":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "group testing an information theory perspective")
  Member "group testing with prior statistics":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "group testing an information theory perspective")
  Member "bitwise map algorithm for group testing based on holographic transformation":
    also connected through other hyperedges:
      (none)
  Member "the capacity of non identical adaptive group testing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "rates of adaptive group testing in the linear regime")
  Member "group testing algorithms bounds and simulations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "serial quantization for sparse time sequences")
  Member "variable length compression allowing errors extended":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the conditional smooth renyi entropy and its applications in guessing and source coding")
  Member "variable length compression allowing errors":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "asymptotic expansions of smooth r e nyi entropies and their applications")
  Member "the capacity of adaptive group testing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the all or nothing behavior of bernoulli group testing")

Question: Please tell me which class the source paper 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)