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: "the capacity of adaptive group testing"
  Abstract: We define capacity for group testing problems and deduce bounds for the capacity of a variety of noisy models, based on the capacity of equivalent noisy communication channels. For noiseless adaptive group testing we prove an information-theoretic lower bound which tightens a bound of Chan et al. This can be combined with a performance analysis of a version of Hwang's adaptive group testing algorithm, in order to deduce the capacity of noiseless and erasure group testing models.

Connected through hyperedge HE-1 (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-2 (induced by source paper "noisy source location on a line"):
  with members:
    - "an adaptivity hierarchy theorem for property testing"
    - "deterministic and probabilistic binary search in graphs"
    - "locating the source of diffusion in large scale networks"

Connected through hyperedge HE-3 (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-4 (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-5 (induced by source paper "an introductory guide to fano s inequality with applications in statistical estimation"):
  with members:
    - "a strong converse bound for multiple hypothesis testing with applications to high dimensional estimation"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "worst case vs average case design for estimation from fixed pairwise comparisons"
    - "an adaptive sublinear time block sparse fourier transform"
    - "lower bounds on active learning for graphical model selection"
    - "f divergence inequalities"
    - "on the difficulty of selecting ising models with approximate recovery"
    - "converse bounds for noisy group testing with arbitrary measurement matrices"

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 "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"

Connected through hyperedge HE-8 (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"

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)