human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "performance of group testing algorithms with near constant tests per item"):
  Abstract: We consider the nonadaptive group testing with    $N$    items, of which    $K = \Theta (N^\theta)$    are defective. We study a test design in which each item appears in nearly the same number of tests. For each item, we independently pick    $L$    tests uniformly at random with replacement and place the item in those tests. We analyze the performance of these designs with simple and practical decoding algorithms in a range of sparsity regimes and show that the performance is consistently improved in comparison with standard Bernoulli designs. We show that our new design requires roughly 23% fewer tests than a Bernoulli design when paired with the simple decoding algorithms known as combinatorial orthogonal matching pursuit and definite defectives (DD). This gives the best known nonadaptive group testing performance for    $\theta > 0.43$    and the best proven performance with a practical decoding algorithm for all    $\theta \in (0,1)$   . We also give a converse result showing that the DD algorithm is optimal with respect to our randomized design when    $\theta > 1/2$   . We complement our theoretical results with simulations that show a notable improvement over Bernoulli designs in both sparse and dense regimes.

Includes members:
  Member "the capacity of bernoulli nonadaptive group testing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the all or nothing behavior of bernoulli group testing")
  Member "on the optimality of some group testing algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "group testing an information theory perspective")
  Member "improved group testing rates with constant column weight designs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "quantitative group testing in the sublinear regime")
  Member "limits on support recovery with probabilistic models an information theoretic framework":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "group testing an information theory perspective")
  Member "partition information and its transmission over boolean multi access channels":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "group testing an information theory perspective")
  Member "saffron a fast efficient and robust framework for group testing based on sparse graph codes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the all or nothing behavior of bernoulli group testing")
  Member "group testing algorithms bounds and simulations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "serial quantization for sparse time sequences")
  Member "sparse signal processing with linear and non linear observations a unified shannon theoretic approach":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "information theoretic lower bounds for compressive sensing with generative models")

Question: Please tell me which class the source paper 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)