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 "improved non adaptive algorithms for threshold group testing with a gap"):
  Abstract: The basic goal of threshold group testing is to identify up to $d$ defective items among a population of $n$ items, where $d$ is usually much smaller than $n$. The outcome of a test on a subset of items is positive if the subset has at least $u$ defective items, negative if it has up to $\ell$ defective items, where $0 \leq \ell < u$, and arbitrary otherwise. This is called threshold group testing with a gap. There are a few reported studies on test designs and decoding algorithms for identifying defective items. Most of the previous studies have not been feasible because there are numerous constraints on their problem settings or the decoding complexities of their proposed schemes are relatively large. Therefore, it is compulsory to reduce the number of tests as well as the decoding complexity, i.e., the time for identifying the defective items, for achieving practical schemes. #R##N#The work presented here makes five contributions. The first is a corrected theorem for a non-adaptive algorithm for threshold group testing proposed by Chen and Fu. The second is an improvement in the construction of disjunct matrices, which are the main tools for tackling (threshold) group testing. Specifically, we present a better upper bound on the number of tests for disjunct matrices compared to related work. The third and fourth contributions are a reduction in the number of tests and a reduction in the decoding time for identifying defective items in a noisy setting on test outcomes. The fifth contribution is a demonstration of the resulting improvements by simulation for previous work and the proposed schemes.

Includes members:
  Member "group testing an information theory perspective":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the all or nothing behavior of bernoulli group testing")
  Member "a framework for generalized group testing with inhibitors and its potential application in neuroscience":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "improved encoding and decoding for non adaptive threshold group testing")
  Member "separable codes for the symmetric multiple access channel":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "improved encoding and decoding for non adaptive threshold group testing")
  Member "efficiently decodable non adaptive threshold group testing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sublinear decoding schemes for non adaptive group testing with inhibitors")
  Member "superimposed codes and threshold group testing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "improved encoding and decoding for non adaptive threshold group testing")
  Member "grotesque noisy group testing quick and efficient":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "improved encoding and decoding for non adaptive threshold group testing")
  Member "improved constructions for non adaptive threshold group testing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "group testing an information theory perspective")
  Member "noise resilient group testing limitations and constructions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on the all or nothing behavior of bernoulli group testing")

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)