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: "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.

Connected through hyperedge HE-1 (induced by source paper "group testing an information theory perspective"):
  with members:
    - "quantitative group testing in the sublinear regime"
    - "understanding phase transitions via mutual information and mmse"
    - "performance of group testing algorithms with near constant tests per item"
    - "learning erd h o s r enyi random graphs via edge detecting queries"
    - "information theoretic and algorithmic thresholds for group testing"
    - "novel impossibility results for group testing"
    - "noisy adaptive group testing bounds and algorithms"
    - "near optimal noisy group testing via separate decoding of items"

Connected through hyperedge HE-2 (induced by source paper "sublinear time non adaptive group testing with o k log n tests via bit mixing coding"):
  with members:
    - "optimal non adaptive group testing"
    - "group testing an information theory perspective"
    - "simple codes and sparse recovery with fast decoding"
    - "performance of group testing algorithms with near constant tests per item"
    - "information theoretic and algorithmic thresholds for group testing"
    - "cross sender bit mixing coding"
    - "on the optimality of the kautz singleton construction in probabilistic group testing"
    - "near optimal noisy group testing via separate decoding of items"

Connected through hyperedge HE-3 (induced by source paper "superset technique for approximate recovery in one bit compressed sensing"):
  with members:
    - "improved bounds for universal one bit compressive sensing"
    - "methods for quantized compressed sensing"
    - "group testing algorithms bounds and simulations"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "boolean compressed sensing and noisy group testing"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"
    - "noise resilient group testing limitations and constructions"

Connected through hyperedge HE-4 (induced by source paper "non adaptive quantitative group testing using irregular sparse graph codes"):
  with members:
    - "optimal multiclass overfitting by sequence reconstruction from hamming queries"
    - "quantitative group testing in the sublinear regime"
    - "sparse graph codes for non adaptive quantitative group testing"
    - "a simple and efficient strategy for the coin weighing problem with a spring scale"
    - "decoding from pooled data phase transitions of message passing"
    - "optimal nested test plan for combinatorial quantitative group testing"

Connected through hyperedge HE-5 (induced by source paper "cross sender bit mixing coding"):
  with members:
    - "non adaptive group testing framework based on concatenation code"
    - "group testing using left and right regular sparse graph codes"
    - "group testing schemes from codes and designs"
    - "saffron a fast efficient and robust framework for group testing based on sparse graph codes"
    - "computing in additive networks with bounded information codes"
    - "group testing algorithms bounds and simulations"
    - "on almost disjunct matrices for group testing"
    - "bounds on contention management in radio networks"

Connected through hyperedge HE-6 (induced by source paper "on the optimality of the kautz singleton construction in probabilistic group testing"):
  with members:
    - "performance of group testing algorithms with near constant tests per item"
    - "noisy non adaptive group testing a near definite defectives approach"
    - "near optimal noisy group testing via separate decoding of items"
    - "nearly optimal sparse group testing"
    - "learning immune defectives graph through group tests"
    - "saffron a fast efficient and robust framework for group testing based on sparse graph codes"
    - "group testing algorithms bounds and simulations"
    - "non adaptive group testing explicit bounds and novel algorithms"

Connected through hyperedge HE-7 (induced by source paper "novel impossibility results for group testing"):
  with members:
    - "individual testing is optimal for nonadaptive group testing in the linear regime"
    - "improved group testing rates with constant column weight designs"
    - "non adaptive group testing explicit bounds and novel algorithms"
    - "approximate sparsity pattern recovery information theoretic lower bounds"
    - "information inequalities for joint distributions with interpretations and applications"

Connected through hyperedge HE-8 (induced by source paper "nearly optimal sparse group testing"):
  with members:
    - "performance of group testing algorithms with near constant tests per item"
    - "novel impossibility results for group testing"
    - "near optimal noisy group testing via separate decoding of items"
    - "individual testing is optimal for nonadaptive group testing in the linear regime"
    - "improved group testing rates with constant column weight designs"
    - "limits on support recovery with probabilistic models an information theoretic framework"
    - "group testing schemes from codes and designs"
    - "nonadaptive group testing with random set of defectives via constant weight codes"

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)