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: "converse bounds for noisy group testing with arbitrary measurement matrices"
  Abstract: We consider the group testing problem, in which one seeks to identify a subset of defective items within a larger set of items based on a number of noisy tests. While matching achievability and converse bounds are known in several cases of interest for i.i.d.~measurement matrices, less is known regarding converse bounds for arbitrary measurement matrices. We address this by presenting two converse bounds for arbitrary matrices and general noise models. First, we provide a strong converse bound ($\mathbb{P}[\mathrm{error}] \to 1$) that matches existing achievability bounds in several cases of interest. Second, we provide a weak converse bound ($\mathbb{P}[\mathrm{error}] \not\to 0$) that matches existing achievability bounds in greater generality.

Connected through hyperedge HE-1 (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-2 (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"
    - "an information diffusion fano inequality"

Connected through hyperedge HE-3 (induced by source paper "near optimal noisy group testing via separate decoding of items"):
  with members:
    - "performance of group testing algorithms with near constant tests per item"
    - "the capacity of bernoulli nonadaptive group testing"
    - "on the optimality of some group testing algorithms"
    - "asymptotics of fingerprinting and group testing tight bounds from channel capacities"
    - "limits on support recovery with probabilistic models an information theoretic framework"
    - "computationally tractable algorithms for finding a subset of non defective items from a large population"
    - "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-4 (induced by source paper "noisy adaptive group testing bounds and algorithms"):
  with members:
    - "novel impossibility results for group testing"
    - "individual testing is optimal for nonadaptive group testing in the linear regime"
    - "the capacity of bernoulli nonadaptive group testing"
    - "the capacity of adaptive group testing"
    - "non adaptive group testing explicit bounds and novel algorithms"
    - "boolean compressed sensing and noisy group testing"
    - "non adaptive probabilistic group testing with noisy measurements near optimal bounds with efficient algorithms"
    - "noise resilient group testing limitations and constructions"

Connected through hyperedge HE-5 (induced by source paper "an active sequential xampling detector for spectrum sensing"):
  with members:
    - "utility maximizing sequential sensing over a finite horizon"
    - "cross layer estimation and control for cognitive radio exploiting sparse network dynamics"
    - "info greedy sequential adaptive compressed sensing"
    - "sub nyquist sampling for power spectrum sensing in cognitive radios a unified approach"
    - "wideband spectrum sensing with sub nyquist sampling in cognitive radios"
    - "boolean compressed sensing and noisy group testing"
    - "on optimality of myopic policy for restless multi armed bandit problem an axiomatic approach"
    - "compressive and noncompressive power spectral density estimation from periodic nonuniform samples"

Connected through hyperedge HE-6 (induced by source paper "active hypothesis testing on a tree anomaly detection under hierarchical observations"):
  with members:
    - "improved target acquisition rates with feedback codes"
    - "optimal nested test plan for combinatorial quantitative group testing"
    - "semiquantitative group testing"
    - "quickest search over multiple sequences with mixed observations"
    - "active sequential hypothesis testing"
    - "sequentiality and adaptivity gains in active hypothesis testing"
    - "controlled sensing for multihypothesis testing"
    - "non adaptive group testing explicit bounds and novel algorithms"

Connected through hyperedge HE-7 (induced by source paper "secure group testing"):
  with members:
    - "the capacity of bernoulli nonadaptive group testing"
    - "limits on support recovery with probabilistic models an information theoretic framework"
    - "strong converses for group testing in the finite blocklength regime"
    - "partition information and its transmission over boolean multi access channels"
    - "information theoretic bounds for adaptive sparse recovery"
    - "the capacity of non identical adaptive group testing"
    - "group testing algorithms bounds and simulations"
    - "the capacity of adaptive group testing"

Connected through hyperedge HE-8 (induced by source paper "limits on support recovery with probabilistic models an information theoretic framework"):
  with members:
    - "asymptotics of fingerprinting and group testing tight bounds from channel capacities"
    - "signal estimation with additive error metrics in compressed sensing"
    - "on the dispersions of three network information theory problems"
    - "group testing algorithms bounds and simulations"
    - "living on the edge phase transitions in convex programs with random data"
    - "sparse signal processing with linear and non linear observations a unified shannon theoretic approach"
    - "the capacity of adaptive group testing"
    - "support recovery with sparsely sampled free random matrices"

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)