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: "individual testing is optimal for nonadaptive group testing in the linear regime"
  Abstract: We consider nonadaptive probabilistic group testing in the linear regime, where each of n items is defective independently with probability p in (0,1), where p is a constant independent of n. We show that testing each item individually is optimal, in the sense that with fewer than n tests the error probability is bounded away from zero.

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 "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-3 (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-4 (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"
    - "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"
    - "group testing with random pools optimal two stage algorithms"

Connected through hyperedge HE-5 (induced by source paper "rates of adaptive group testing in the linear regime"):
  with members:
    - "group testing an information theory perspective"
    - "novel impossibility results for group testing"
    - "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-6 (induced by source paper "information theoretic and algorithmic thresholds for group testing"):
  with members:
    - "performance of group testing algorithms with near constant tests per item"
    - "the capacity of bernoulli nonadaptive group testing"
    - "the computer science and physics of community detection landscapes phase transitions and hardness"
    - "on the optimality of some group testing algorithms"
    - "improved group testing rates with constant column weight designs"
    - "group testing algorithms bounds and simulations"

Connected through hyperedge HE-7 (induced by source paper "noisy adaptive group testing bounds and algorithms"):
  with members:
    - "novel impossibility results for group testing"
    - "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-8 (induced by source paper "novel impossibility results for group testing"):
  with members:
    - "improved group testing rates with constant column weight designs"
    - "nonadaptive group testing with random set of defectives"
    - "non adaptive group testing explicit bounds and novel algorithms"
    - "approximate sparsity pattern recovery information theoretic lower bounds"
    - "information inequalities for joint distributions with interpretations and applications"

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)