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: "non adaptive probabilistic group testing with noisy measurements near optimal bounds with efficient algorithms"
  Abstract: We consider the problem of detecting a small subset of defective items from a large set via non-adaptive "random pooling" group tests. We consider both the case when the measurements are noiseless, and the case when the measurements are noisy (the outcome of each group test may be independently faulty with probability q). Order-optimal results for these scenarios are known in the literature. We give information-theoretic lower bounds on the query complexity of these problems, and provide corresponding computationally efficient algorithms that match the lower bounds up to a constant factor. To the best of our knowledge this work is the first to explicitly estimate such a constant that characterizes the gap between the upper and lower bounds for these problems.

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"
    - "group testing algorithms bounds and simulations"

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 "performance of group testing algorithms with near constant tests per item"):
  with members:
    - "the capacity of bernoulli nonadaptive group testing"
    - "on the optimality of some group testing algorithms"
    - "improved group testing rates with constant column weight designs"
    - "limits on support recovery with probabilistic models an information theoretic framework"
    - "partition information and its transmission over boolean multi access channels"
    - "saffron a fast efficient and robust framework for group testing based on sparse graph codes"
    - "group testing algorithms bounds and simulations"
    - "sparse signal processing with linear and non linear observations a unified shannon theoretic approach"

Connected through hyperedge HE-8 (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"
    - "nonadaptive group testing with random set of defectives"
    - "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"

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)