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: "on almost disjunct matrices for group testing"
  Abstract: In a group testing scheme, a set of tests is designed to identify a small number t of defective items among a large set (of size N) of items. In the non-adaptive scenario the set of tests has to be designed in one-shot. In this setting, designing a testing scheme is equivalent to the construction of a disjunct matrix, an M ×N 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 minimum possible number M of rows (tests). One of the main ways of constructing disjunct matrices relies on constant weight error-correcting codes and their minimum distance. In this paper, we consider a relaxed definition of a disjunct matrix known as almost disjunct matrix. This concept is also studied under the name of weakly separated design in the literature. The relaxed definition allows one to come up with group testing schemes where a close-to-one fraction of all possible sets of defective items are identifiable. Our main contribution is twofold. First, we go beyond the minimum distance analysis and connect the average distance of a constant weight code to the parameters of an almost disjunct matrix constructed from it. Next we show as a consequence an explicit construction of almost disjunct matrices based on our average distance analysis. The parameters of our construction can be varied to cover a large range of relations for t and N. As an example of parameters, consider any absolute constant e > 0 and t proportional to N δ , δ > 0. With our method it is possible to explicitly construct a group testing scheme that identifies (1 − e) proportion of all possible defective sets of size t using only \(O\Big(t^{3/2}\sqrt{ \log(N/\epsilon)}\Big)\) tests (as opposed to O(t 2logN) required to identify all defective sets).

Connected through hyperedge HE-1 (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"
    - "nonadaptive group testing with random set of defectives"
    - "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"
    - "bounds on contention management in radio networks"

Connected through hyperedge HE-2 (induced by source paper "low rank approximation and decomposition of large matrices using error correcting codes"):
  with members:
    - "fast estimation of approximate matrix ranks using spectral densities"
    - "randomized block krylov methods for stronger and faster approximate singular value decomposition"
    - "restricted isometry property of random subdictionaries"
    - "implementing randomized matrix algorithms in parallel and distributed environments"
    - "sketching as a tool for numerical linear algebra"
    - "low rank matrix approximation in linear time"
    - "low rank approximation and regression in input sparsity time"
    - "improved matrix algorithms via the subsampled randomized hadamard transform"

Connected through hyperedge HE-3 (induced by source paper "nonadaptive group testing with random set of defectives"):
  with members:
    - "poisson group testing a probabilistic model for boolean compressed sensing"
    - "grotesque noisy group testing quick and efficient"
    - "non adaptive group testing explicit bounds and novel algorithms"
    - "boolean compressed sensing and noisy group testing"
    - "improved constructions for non adaptive threshold group testing"
    - "construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property"
    - "noise resilient group testing limitations and constructions"
    - "explicit non adaptive combinatorial group testing schemes"

Connected through hyperedge HE-4 (induced by source paper "nonadaptive group testing with random set of defectives via constant weight codes"):
  with members:
    - "poisson group testing a probabilistic model for boolean compressed sensing"
    - "grotesque noisy group testing quick and efficient"
    - "non adaptive group testing explicit bounds and novel algorithms"
    - "boolean compressed sensing and noisy group testing"
    - "improved constructions for non adaptive threshold group testing"
    - "construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property"
    - "noise resilient group testing limitations and constructions"
    - "explicit non adaptive combinatorial group testing schemes"

Connected through hyperedge HE-5 (induced by source paper "almost disjunct matrices from codes and designs"):
  with members:
    - "nonadaptive group testing with random set of defectives via constant weight codes"
    - "bounds on the rate of disjunctive codes"
    - "semiquantitative group testing"
    - "non adaptive group testing explicit bounds and novel algorithms"
    - "semi quantitative group testing"
    - "explicit non adaptive combinatorial group testing schemes"

Connected through hyperedge HE-6 (induced by source paper "group testing schemes from codes and designs"):
  with members:
    - "nonadaptive group testing with random set of defectives"
    - "nonadaptive group testing with random set of defectives via constant weight codes"
    - "almost cover free codes and designs"
    - "bounds on the rate of disjunctive codes"
    - "semiquantitative group testing"

Connected through hyperedge HE-7 (induced by source paper "grotesque noisy group testing quick and efficient"):
  with members:
    - "boolean compressed sensing and noisy group testing"
    - "non adaptive probabilistic group testing with noisy measurements near optimal bounds with efficient algorithms"
    - "improved adaptive group testing algorithms with applications to multiple access channels and dead sensor diagnosis"
    - "group testing with random pools optimal two stage algorithms"
    - "explicit non adaptive combinatorial group testing schemes"

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)