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: "semiquantitative group testing"
  Abstract: We propose a novel group testing method, termed semiquantitative group testing (SQGT), motivated by a class of problems arising in genome screening experiments. The SQGT is a (possibly) nonbinary pooling scheme that may be viewed as a concatenation of an adder channel and an integer-valued quantizer. In its full generality, SQGT may be viewed as a unifying framework for group testing, in the sense that most group testing models are special instances of SQGT. For the new testing scheme, we define the notion of SQ-disjunct and SQ-separable codes, representing generalizations of classical disjunct and separable codes. We describe several combinatorial and probabilistic constructions for such codes. While for most of these constructions, we assume that the number of defectives is much smaller than total number of test subjects, we also consider the case in which there is no restriction on the number of defectives and they may be as large as the total number of subjects. For the codes constructed in this paper, we describe a number of efficient decoding algorithms. In addition, we describe a belief propagation decoder for sparse SQGT codes for which no other efficient decoder is currently known.

Connected through hyperedge HE-1 (induced by source paper "group testing with runlength constraints for topological molecular storage"):
  with members:
    - "quantitative group testing in the sublinear regime"
    - "non adaptive quantitative group testing using irregular sparse graph codes"
    - "group testing an information theory perspective"
    - "performance of group testing algorithms with near constant tests per item"
    - "the capacity of bernoulli nonadaptive group testing"
    - "sparse combinatorial group testing"
    - "decoding from pooled data phase transitions of message passing"
    - "nearly optimal sparse group testing"

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 "a simple and efficient strategy for the coin weighing problem with a spring scale"):
  with members:
    - "optimal nested test plan for combinatorial quantitative group testing"

Connected through hyperedge HE-4 (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"
    - "optimal nested test plan for combinatorial quantitative 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"

Connected through hyperedge HE-5 (induced by source paper "almost disjunct matrices from codes and designs"):
  with members:
    - "nonadaptive group testing with random set of defectives via constant weight codes"
    - "bounds on the rate of disjunctive codes"
    - "on almost disjunct matrices for group testing"
    - "non adaptive group testing explicit bounds and novel algorithms"
    - "semi quantitative group testing"
    - "explicit non adaptive combinatorial group testing schemes"

Connected through hyperedge HE-6 (induced by source paper "group testing schemes from codes and designs"):
  with members:
    - "nonadaptive group testing with random set of defectives"
    - "nonadaptive group testing with random set of defectives via constant weight codes"
    - "almost cover free codes and designs"
    - "bounds on the rate of disjunctive codes"
    - "on almost disjunct matrices for group testing"

Connected through hyperedge HE-7 (induced by source paper "learning immune defectives graph through group tests"):
  with members:
    - "non adaptive group testing with inhibitors"
    - "non adaptive group testing explicit bounds and novel algorithms"
    - "two models of nonadaptive group testing for designing screening experiments"

Connected through hyperedge HE-8 (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"
    - "group testing with prior statistics"
    - "lectures on designing screening experiments"
    - "the capacity of non identical adaptive group testing"
    - "group testing algorithms bounds and simulations"
    - "non adaptive group testing explicit bounds and novel algorithms"
    - "boolean compressed sensing and noisy group testing"
    - "semi quantitative group testing"

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)