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: "semi quantitative group testing"
  Abstract: We consider a novel group testing procedure, termed semi-quantitative group testing, motivated by a class of problems arising in genome sequence processing. Semi-quantitative group testing (SQGT) is a non-binary pooling scheme that may be viewed as a combination of an adder model followed by a quantizer. For the new testing scheme we define the capacity and evaluate the capacity for some special choices of parameters using information theoretic methods. We also define a new class of disjunct codes suitable for SQGT, termed SQ-disjunct codes. We also provide both explicit and probabilistic code construction methods for SQGT with simple decoding algorithms.

Connected through hyperedge HE-1 (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"
    - "semiquantitative group testing"
    - "on almost disjunct matrices for group testing"
    - "non adaptive group testing explicit bounds and novel algorithms"
    - "explicit non adaptive combinatorial group testing schemes"

Connected through hyperedge HE-2 (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"
    - "semiquantitative group testing"
    - "non adaptive group testing explicit bounds and novel algorithms"
    - "boolean compressed sensing and noisy group testing"

Connected through hyperedge HE-3 (induced by source paper "semiquantitative group testing"):
  with members:
    - "lectures on designing screening experiments"
    - "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"
    - "improved constructions for non adaptive threshold group testing"
    - "note on noisy group testing asymptotic bounds and belief propagation reconstruction"
    - "weighted superimposed codes and constrained integer compressed sensing"
    - "fast min sum algorithms for decoding of ldpc over gf q"

Connected through hyperedge HE-4 (induced by source paper "code construction and decoding algorithms for semi quantitative group testing with nonuniform thresholds"):
  with members:
    - "non adaptive group testing with inhibitors"
    - "lectures on designing screening experiments"
    - "semiquantitative group testing"
    - "non adaptive group testing explicit bounds and novel algorithms"
    - "weighted superimposed codes and constrained integer compressed sensing"

Connected through hyperedge HE-5 (induced by source paper "superimposed codes and threshold group testing"):
  with members:
    - "lectures on designing screening experiments"
    - "semiquantitative group testing"
    - "improved constructions for non adaptive threshold group testing"
    - "two models of nonadaptive group testing for designing screening experiments"

Connected through hyperedge HE-6 (induced by source paper "multi group testing for items with real valued status under standard arithmetic"):
  with members:
    - "lectures on designing screening experiments"
    - "semiquantitative group testing"
    - "boolean compressed sensing and noisy group testing"
    - "two models of nonadaptive group testing for designing screening experiments"

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)