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: "on the optimality of some group testing algorithms"
  Abstract: We consider Bernoulli nonadaptive group testing with k = Θ(ηθ) defectives, for θ ∊ (0,1). The practical definite defectives (DD) detection algorithm is known to be optimal for θ > 1/2. We give a new upper bound on the rate of DD, showing that DD is strictly suboptimal for θ < 0.41. We also show that the SCOMP algorithm and algorithms based on linear programming achieve a rate at least as high as DD, so in particular are also optimal for θ ≥ 1/2.

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 "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"
    - "improved group testing rates with constant column weight designs"
    - "saffron a fast efficient and robust framework for group testing based on sparse graph codes"

Connected through hyperedge HE-4 (induced by source paper "performance of group testing algorithms with near constant tests per item"):
  with members:
    - "the capacity of bernoulli nonadaptive group testing"
    - "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"
    - "the capacity of adaptive group testing"

Connected through hyperedge HE-5 (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"
    - "individual testing is optimal for nonadaptive group testing in the linear regime"
    - "the capacity of bernoulli nonadaptive group testing"
    - "the computer science and physics of community detection landscapes phase transitions and hardness"
    - "improved group testing rates with constant column weight designs"
    - "group testing algorithms bounds and simulations"

Connected through hyperedge HE-6 (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"
    - "converse bounds for noisy group testing with arbitrary measurement matrices"
    - "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-7 (induced by source paper "the capacity of bernoulli nonadaptive group testing"):
  with members:
    - "performance of group testing algorithms with near constant tests per item"
    - "limits on support recovery with probabilistic models an information theoretic framework"
    - "limits on support recovery with probabilistic models an information spectrum approach"
    - "group testing algorithms bounds and simulations"
    - "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"

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)