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: "optimal nested test plan for combinatorial quantitative group testing"
  Abstract: We consider the quantitative group testing problem where the objective is to identify defective items in a given population based on results of tests performed on subsets of the population. Under the quantitative group testing model, the result of each test reveals the number of defective items in the tested group. The minimum number of tests achievable by nested test plans was established by Aigner and Schughart in 1985 within a minimax framework. The optimal nested test plan offering this performance, however, was not obtained. In this work, we establish the optimal nested test plan in closed form. This optimal nested test plan is also order optimal among all test plans as the population size approaches infinity. Using heavy-hitter detection as a case study, we show via simulation examples orders of magnitude improvement of the group testing approach over two prevailing sampling-based approaches in detection accuracy and counter consumption. Other applications include anomaly detection and wideband spectrum sensing in cognitive radio systems.

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 "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"
    - "on the optimality of some group testing algorithms"
    - "improved group testing rates with constant column weight designs"

Connected through hyperedge HE-3 (induced by source paper "non adaptive quantitative group testing using irregular sparse graph codes"):
  with members:
    - "optimal multiclass overfitting by sequence reconstruction from hamming queries"
    - "quantitative group testing in the sublinear regime"
    - "sparse graph codes for non adaptive quantitative group testing"
    - "a simple and efficient strategy for the coin weighing problem with a spring scale"
    - "decoding from pooled data phase transitions of message passing"
    - "nonadaptive group testing with random set of defectives"

Connected through hyperedge HE-4 (induced by source paper "sparse graph codes for non adaptive quantitative group testing"):
  with members:
    - "a simple and efficient strategy for the coin weighing problem with a spring scale"
    - "a fast noniterative algorithm for compressive sensing using binary measurement matrices"
    - "group testing using left and right regular sparse graph codes"
    - "saffron a fast efficient and robust framework for group testing based on sparse graph codes"
    - "sub linear time support recovery for compressed sensing using sparse graph codes"
    - "compressed sensing with sparse binary matrices instance optimal error guarantees in near optimal time"

Connected through hyperedge HE-5 (induced by source paper "a simple and efficient strategy for the coin weighing problem with a spring scale"):
  with members:
    - "semiquantitative group testing"

Connected through hyperedge HE-6 (induced by source paper "active hypothesis testing on a tree anomaly detection under hierarchical observations"):
  with members:
    - "improved target acquisition rates with feedback codes"
    - "converse bounds for noisy group testing with arbitrary measurement matrices"
    - "semiquantitative group testing"
    - "quickest search over multiple sequences with mixed observations"
    - "active sequential hypothesis testing"
    - "sequentiality and adaptivity gains in active hypothesis testing"
    - "controlled sensing for multihypothesis testing"
    - "non adaptive group testing explicit bounds and novel 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)