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 prior statistics"
  Abstract: We consider a new group testing model wherein each item is a binary random variable defined by an a priori probability of being defective. We assume that each probability is small and that items are independent, but not necessarily identically distributed. The goal of group testing algorithms is to identify with high probability the subset of defectives via non-linear (disjunctive) binary measurements. Our main contributions are two classes of algorithms: (1) adaptive algorithms with tests based either on a maximum entropy principle, or on a Shannon-Fano/Huffman code; (2) non-adaptive algorithms. Under loose assumptions and with high probability, our algorithms only need a number of measurements that is close to the information-theoretic lower bound, up to an explicitly-calculated universal constant factor. We provide simulations to support our results.

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 "nearly optimal sparse group testing"):
  with members:
    - "performance of group testing algorithms with near constant tests per item"
    - "novel impossibility results for group testing"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "strong converses for group testing in the finite blocklength regime"):
  with members:
    - "limits on support recovery with probabilistic models an information theoretic framework"
    - "bitwise map algorithm for group testing based on holographic transformation"
    - "the capacity of non identical adaptive group testing"
    - "group testing algorithms bounds and simulations"
    - "variable length compression allowing errors extended"
    - "variable length compression allowing errors"
    - "the capacity of adaptive group testing"
    - "adaptive group testing as channel coding with feedback"

Connected through hyperedge HE-4 (induced by source paper "poisson group testing a probabilistic model for boolean compressed sensing"):
  with members:
    - "limits on support recovery with probabilistic models an information theoretic framework"
    - "lectures on designing screening experiments"
    - "the capacity of non identical adaptive group testing"
    - "group testing algorithms bounds and simulations"
    - "semiquantitative group testing"
    - "non adaptive group testing explicit bounds and novel algorithms"
    - "boolean compressed sensing and noisy group testing"
    - "semi quantitative group testing"

Connected through hyperedge HE-5 (induced by source paper "probabilistic group testing under sum observations a parallelizable 2 approximation for entropy loss"):
  with members:
    - "info greedy sequential adaptive compressed sensing"
    - "quantitative group testing for heavy hitter detection"
    - "active testing for face detection and localization"
    - "improved combinatorial group testing algorithms for real world problem sizes"
    - "an informational approach to the global optimization of expensive to evaluate functions"

Connected through hyperedge HE-6 (induced by source paper "the capacity of non identical adaptive group testing"):
  with members:
    - "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"
    - "boolean compressed sensing and noisy group testing"
    - "note on noisy group testing asymptotic bounds and belief propagation reconstruction"

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)