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 with random pools optimal two stage algorithms"
  Abstract: We study Probabilistic Group Testing of a set of N items each of which is defective with probability p. We focus on the double limit of small defect probability, p >1, taking either p->0 after $N\to\infty$ or $p=1/N^{\beta}$ with $\beta\in(0,1/2)$. In both settings the optimal number of tests which are required to identify with certainty the defectives via a two-stage procedure, $\bar T(N,p)$, is known to scale as $Np|\log p|$. Here we determine the sharp asymptotic value of $\bar T(N,p)/(Np|\log p|)$ and construct a class of two-stage algorithms over which this optimal value is attained. This is done by choosing a proper bipartite regular graph (of tests and variable nodes) for the first stage of the detection. Furthermore we prove that this optimal value is also attained on average over a random bipartite graph where all variables have the same degree, while the tests have Poisson-distributed degrees. Finally, we improve the existing upper and lower bound for the optimal number of tests in the case $p=1/N^{\beta}$ with $\beta\in[1/2,1)$.

Connected through hyperedge HE-1 (induced by source paper "optimal multistage group testing algorithm for 3 defectives"):
  with members:
    - "a new algorithm for two stage group testing"
    - "performance of group testing algorithms with near constant tests per item"
    - "on a hypergraph approach to multistage group testing problems"
    - "lectures on designing screening experiments"
    - "bounds on the rate of disjunctive codes"

Connected through hyperedge HE-2 (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-3 (induced by source paper "optimal adaptive group testing"):
  with members:
    - "optimal non adaptive group testing"
    - "rates of adaptive group testing in the linear regime"
    - "information theoretic and algorithmic thresholds for group testing"
    - "noisy adaptive group testing bounds and algorithms"
    - "individual testing is optimal for nonadaptive group testing in the linear regime"
    - "the capacity of bernoulli nonadaptive group testing"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"

Connected through hyperedge HE-4 (induced by source paper "optimal non adaptive group testing"):
  with members:
    - "group testing an information theory perspective"
    - "performance of group testing algorithms with near constant tests per item"
    - "decoding from pooled data phase transitions of message passing"
    - "threshold saturation for spatially coupled ldpc and ldgm codes on bms channels"
    - "group testing algorithms bounds and simulations"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"

Connected through hyperedge HE-5 (induced by source paper "a new algorithm for two stage group testing"):
  with members:
    - "performance of group testing algorithms with near constant tests per item"
    - "on a hypergraph approach to multistage group testing problems"
    - "lectures on designing screening experiments"
    - "bounds on the rate of disjunctive codes"

Connected through hyperedge HE-6 (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"
    - "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"

Connected through hyperedge HE-7 (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"
    - "novel impossibility results for group testing"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "on a hypergraph approach to multistage group testing problems"):
  with members:
    - "bounds on the rate of disjunctive codes in russian"
    - "lectures on designing screening experiments"
    - "bounds on the rate of disjunctive codes"

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.DS(Data Structures and Algorithms)