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: "group testing algorithms bounds and simulations"
  Abstract: We consider the problem of nonadaptive noiseless group testing of    $N$    items of which    $K$    are defective. We describe four detection algorithms, the    ${\tt COMP}$    algorithm of Chan  et al.  , two new algorithms,    ${\tt DD}$    and    ${\tt SCOMP}$   , which require stronger evidence to declare an item defective, and an essentially optimal but computationally difficult algorithm called    ${\tt SSS}$   . We consider an important class of designs for the group testing problem, namely those in which the test structure is given via a Bernoulli random process. In this class of Bernoulli designs, by considering the asymptotic rate of these algorithms, we show that    ${\tt DD}$    outperforms    ${\tt COMP}$   , that    ${\tt DD}$    is essentially optimal in regimes where    $K\geq\sqrt N$   , and that no algorithm can perform as well as the best nonrandom adaptive algorithms when    $K>N^{0.35}$   . In simulations, we see that    ${\tt DD}$    and    ${\tt SCOMP}$    far outperform    ${\tt COMP}$   , with    ${\tt SCOMP}$    very close to the optimal    ${\tt SSS}$   , especially in cases with larger    $K$   .

Connected through hyperedge HE-1 (induced by source paper "serial quantization for sparse time sequences"):
  with members:
    - "learning distributions from their samples under communication constraints"
    - "information theoretic and algorithmic thresholds for group testing"
    - "efficient data collection over multiple access wireless sensors network"
    - "efficient nonrandom construction and decoding of non adaptive group testing"
    - "quantization of compressive samples with stable and robust recovery"
    - "quantization and compressive sensing"
    - "distributed quantization for measurement of correlated sparse sources over noisy channels"
    - "distributed compressed sensing for static and time varying networks"

Connected through hyperedge HE-2 (induced by source paper "on the all or nothing behavior of bernoulli group testing"):
  with members:
    - "optimal non adaptive group testing"
    - "group testing an information theory perspective"
    - "performance of group testing algorithms with near constant tests per item"
    - "information theoretic and algorithmic thresholds for group testing"
    - "near optimal noisy group testing via separate decoding of items"
    - "the capacity of bernoulli nonadaptive group testing"
    - "saffron a fast efficient and robust framework for group testing based on sparse graph codes"
    - "the capacity of adaptive group testing"

Connected through hyperedge HE-3 (induced by source paper "group testing with runlength constraints for topological molecular storage"):
  with members:
    - "quantitative group testing in the sublinear regime"
    - "non adaptive quantitative group testing using irregular sparse graph codes"
    - "group testing an information theory perspective"
    - "performance of group testing algorithms with near constant tests per item"
    - "the capacity of bernoulli nonadaptive group testing"
    - "sparse combinatorial group testing"
    - "decoding from pooled data phase transitions of message passing"
    - "nearly optimal sparse group testing"

Connected through hyperedge HE-4 (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-5 (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-6 (induced by source paper "learning erd h o s r enyi random graphs via edge detecting queries"):
  with members:
    - "an introductory guide to fano s inequality with applications in statistical estimation"
    - "group testing an information theory perspective"
    - "performance of group testing algorithms with near constant tests per item"
    - "information theoretic and algorithmic thresholds for group testing"
    - "on the optimality of the kautz singleton construction in probabilistic group testing"
    - "on learning graphs with edge detecting queries"
    - "noisy non adaptive group testing a near definite defectives approach"
    - "near optimal noisy group testing via separate decoding of items"

Connected through hyperedge HE-7 (induced by source paper "serial quantization for representing sparse signals"):
  with members:
    - "learning distributions from their samples under communication constraints"
    - "hardware limited task based quantization"
    - "efficient data collection over multiple access wireless sensors network"
    - "quantization of compressive samples with stable and robust recovery"
    - "quantization and compressive sensing"
    - "spatial compressive sensing for mimo radar"
    - "quantized iterative hard thresholding bridging 1 bit and high resolution quantized compressed sensing"
    - "gespar efficient phase retrieval of sparse signals"

Connected through hyperedge HE-8 (induced by source paper "quantitative group testing in the sublinear regime"):
  with members:
    - "sparse graph codes for non adaptive quantitative group testing"
    - "performance of group testing algorithms with near constant tests per item"
    - "information theoretic and algorithmic thresholds for group testing"
    - "information theoretic thresholds from the cavity method"
    - "the capacity of bernoulli nonadaptive group testing"
    - "decoding from pooled data phase transitions of message passing"
    - "on the optimality of some group testing algorithms"
    - "improved group testing rates with constant column weight designs"

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)