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: "novel impossibility results for group testing"
  Abstract: In this work we prove non-trivial impossibility results for perhaps the simplest non-linear estimation problem, that of {\it Group Testing} (GT), via the recently developed Madiman-Tetali inequalities. Group Testing concerns itself with identifying a hidden set of $d$ defective items from a set of $n$ items via $t$ {disjunctive/pooled} measurements ("group tests"). We consider the linear sparsity regime, i.e. $d = \delta n$ for any constant $\delta >0$, a hitherto little-explored (though natural) regime. In a standard information-theoretic setting, where the tests are required to be non-adaptive and a small probability of reconstruction error is allowed, our lower bounds on $t$ are the {\it first} that improve over the classical counting lower bound, $t/n \geq H(\delta)$, where $H(\cdot)$ is the binary entropy function. As corollaries of our result, we show that (i) for $\delta \gtrsim 0.347$, individual testing is essentially optimal, i.e., $t \geq n(1-o(1))$; and (ii) there is an {adaptivity gap}, since for $\delta \in (0.3471,0.3819)$ known {adaptive} GT algorithms require fewer than $n$ tests to reconstruct ${\cal D}$, whereas our bounds imply that the best nonadaptive algorithm must essentially be individual testing of each element. Perhaps most importantly, our work provides a framework for combining combinatorial and information-theoretic methods for deriving non-trivial lower bounds for a variety of non-linear estimation problems.

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"
    - "noisy adaptive group testing bounds and algorithms"
    - "near optimal noisy group testing via separate decoding of items"
    - "individual testing is optimal for nonadaptive group testing in the linear regime"

Connected through hyperedge HE-2 (induced by source paper "rates of adaptive group testing in the linear regime"):
  with members:
    - "group testing an information theory perspective"
    - "individual testing is optimal for nonadaptive group testing in the linear regime"
    - "limits on support recovery with probabilistic models an information theoretic framework"
    - "the capacity of non identical adaptive group testing"
    - "group testing algorithms bounds and simulations"
    - "the capacity of adaptive group testing"
    - "boolean compressed sensing and noisy group testing"

Connected through hyperedge HE-3 (induced by source paper "noisy adaptive group testing bounds and algorithms"):
  with members:
    - "individual testing is optimal for nonadaptive group testing in the linear regime"
    - "the capacity of bernoulli nonadaptive group testing"
    - "converse bounds for noisy group testing with arbitrary measurement matrices"
    - "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-4 (induced by source paper "individual testing is optimal for nonadaptive group testing in the linear regime"):
  with members:
    - "performance of group testing algorithms with near constant tests per item"
    - "limits on support recovery with probabilistic models an information theoretic framework"
    - "the capacity of non identical adaptive group testing"
    - "group testing algorithms bounds and simulations"
    - "the capacity of adaptive group testing"
    - "boolean compressed sensing and noisy group testing"
    - "non adaptive probabilistic group testing with noisy measurements near optimal bounds with efficient algorithms"

Connected through hyperedge HE-5 (induced by source paper "nearly optimal sparse group testing"):
  with members:
    - "performance of group testing algorithms with near constant tests per item"
    - "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"
    - "nonadaptive group testing with random set of defectives via constant weight codes"

Connected through hyperedge HE-6 (induced by source paper "hypothesis test for upper bound on the size of random defective set"):
  with members:
    - "performance of group testing algorithms with near constant tests per item"
    - "individual testing is optimal for nonadaptive group testing in the linear regime"
    - "constraining the number of positive responses in adaptive non adaptive and two stage group testing"
    - "limits on support recovery with probabilistic models an information theoretic framework"
    - "group testing algorithms bounds and simulations"
    - "group testing with random pools optimal two stage algorithms"

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)