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: "grotesque noisy group testing quick and efficient"
  Abstract: Group-testing refers to the problem of identifying (with high probability) a (small) subset of $D$ defectives from a (large) set of $N$ items via a "small" number of "pooled" tests. For ease of presentation in this work we focus on the regime when $D = \cO{N^{1-\gap}}$ for some $\gap > 0$. The tests may be noiseless or noisy, and the testing procedure may be adaptive (the pool defining a test may depend on the outcome of a previous test), or non-adaptive (each test is performed independent of the outcome of other tests). A rich body of literature demonstrates that $\Theta(D\log(N))$ tests are information-theoretically necessary and sufficient for the group-testing problem, and provides algorithms that achieve this performance. However, it is only recently that reconstruction algorithms with computational complexity that is sub-linear in $N$ have started being investigated (recent work by \cite{GurI:04,IndN:10, NgoP:11} gave some of the first such algorithms). In the scenario with adaptive tests with noisy outcomes, we present the first scheme that is simultaneously order-optimal (up to small constant factors) in both the number of tests and the decoding complexity ($\cO{D\log(N)}$ in both the performance metrics). The total number of stages of our adaptive algorithm is "small" ($\cO{\log(D)}$). Similarly, in the scenario with non-adaptive tests with noisy outcomes, we present the first scheme that is simultaneously near-optimal in both the number of tests and the decoding complexity (via an algorithm that requires $\cO{D\log(D)\log(N)}$ tests and has a decoding complexity of {${\cal O}(D(\log N+\log^{2}D))$}. Finally, we present an adaptive algorithm that only requires 2 stages, and for which both the number of tests and the decoding complexity scale as {${\cal O}(D(\log N+\log^{2}D))$}. For all three settings the probability of error of our algorithms scales as $\cO{1/(poly(D)}$.

Connected through hyperedge HE-1 (induced by source paper "improved non adaptive algorithms for threshold group testing with a gap"):
  with members:
    - "group testing an information theory perspective"
    - "a framework for generalized group testing with inhibitors and its potential application in neuroscience"
    - "separable codes for the symmetric multiple access channel"
    - "efficiently decodable non adaptive threshold group testing"
    - "superimposed codes and threshold group testing"
    - "improved constructions for non adaptive threshold group testing"
    - "noise resilient group testing limitations and constructions"

Connected through hyperedge HE-2 (induced by source paper "improved encoding and decoding for non adaptive threshold group testing"):
  with members:
    - "a framework for generalized group testing with inhibitors and its potential application in neuroscience"
    - "efficient nonrandom construction and decoding of non adaptive group testing"
    - "separable codes for the symmetric multiple access channel"
    - "superimposed codes and threshold group testing"
    - "improved constructions for non adaptive threshold group testing"
    - "noise resilient group testing limitations and constructions"
    - "explicit non adaptive combinatorial group testing schemes"

Connected through hyperedge HE-3 (induced by source paper "efficient and error tolerant schemes for non adaptive complex group testing and its application in complex disease genetics"):
  with members:
    - "a framework for generalized group testing with inhibitors and its potential application in neuroscience"
    - "efficient nonrandom construction and decoding of non adaptive group testing"
    - "improved constructions for non adaptive threshold group testing"
    - "noise resilient group testing limitations and constructions"
    - "explicit non adaptive combinatorial group testing schemes"

Connected through hyperedge HE-4 (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"
    - "nonadaptive group testing with random set of defectives"
    - "limits on support recovery with probabilistic models an information theoretic framework"
    - "group testing schemes from codes and designs"

Connected through hyperedge HE-5 (induced by source paper "improved bounds for universal one bit compressive sensing"):
  with members:
    - "nonadaptive group testing with random set of defectives"
    - "one bit compressed sensing by greedy algorithms"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "non adaptive group testing explicit bounds and novel algorithms"
    - "boolean compressed sensing and noisy group testing"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"

Connected through hyperedge HE-6 (induced by source paper "efficiently decodable non adaptive threshold group testing"):
  with members:
    - "superimposed codes and threshold group testing"
    - "improved constructions for non adaptive threshold group testing"
    - "noise resilient group testing limitations and constructions"
    - "explicit non adaptive combinatorial group testing schemes"

Connected through hyperedge HE-7 (induced by source paper "nonadaptive group testing with random set of defectives"):
  with members:
    - "poisson group testing a probabilistic model for boolean compressed sensing"
    - "on almost disjunct matrices for group testing"
    - "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-8 (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"
    - "on almost disjunct matrices for group testing"
    - "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"

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)