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 "nonadaptive group testing with random set of defectives"):
  Abstract: In a  group testing  scheme, a series of  tests  are designed to identify a small number    $t$    of defective items that are present among a large number    $N$    of items. Each test takes a group of items as input and produces a binary output indicating whether any defective item is present in the group. In a non-adaptive scheme, the tests have to be designed in one-shot. In this setting, designing a testing scheme is equivalent to the construction of a  disjunct matrix , an    $M \times N$    binary matrix where the union of supports of any    $t$    columns does not contain the support of any other column. In principle, one wants to have such a matrix with a minimum possible number    $M$    of rows. In this paper, we consider the scenario where defective items are random and follow simple probability distributions. In particular, we consider the cases where: 1) each item can be defective independently with probability    $\frac {t}{N}$    and 2) each    $t$   -set of items can be defective with uniform probability. In both the cases, our aim is to design a testing matrix that successfully identifies the set of defectives with high probability. Both of these models have been studied in the literature before, and it is known that    $\Theta (t \log N)$    tests are necessary as well as sufficient (via random coding) in both the cases. Our main focus is explicit deterministic construction of the test matrices amenable to above scenarios. One of the most popular ways of constructing test matrices relies on  constant-weight error-correcting codes  and their  minimum distance . In particular, it is known that codes result in test matrices with    $O(t^{2} \log N)$    rows that identify any    $t$    defectives. We go beyond the minimum distance analysis and connect the  average distance  of a constant weight code to the parameters of the resulting test matrix. Indeed, we show how distance, a pairwise property of the columns of the matrix, translates to a    $(t+1)$   -wise property of the columns. With our relaxed requirements, we show that using explicit constant-weight codes (e.g., based on algebraic geometry codes) we may achieve a number of tests equal to    $O(t ({\log ^{2} N}/{ \log t}))$    for both the first and second cases. While only away by a factor of    $({\log N}/{\log t})$    from the optimal number of tests, this is the best set of parameters that one can obtain from a deterministic construction, and our main contribution lies in relating the group testing properties to the average and minimum distances of constant-weight codes.

Includes members:
  Member "poisson group testing a probabilistic model for boolean compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "nonadaptive group testing with random set of defectives via constant weight codes")
  Member "grotesque noisy group testing quick and efficient":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "improved non adaptive algorithms for threshold group testing with a gap")
  Member "on almost disjunct matrices for group testing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cross sender bit mixing coding")
  Member "non adaptive group testing explicit bounds and novel algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "serial quantization for sparse time sequences")
  Member "boolean compressed sensing and noisy group testing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "serial quantization for sparse time sequences")
  Member "improved constructions for non adaptive threshold group testing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "improved non adaptive algorithms for threshold group testing with a gap")
  Member "construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "frame moments and welch bound with erasures")
  Member "noise resilient group testing limitations and constructions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)