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 "a framework for generalized group testing with inhibitors and its potential application in neuroscience"):
  Abstract: The main goal of group testing with inhibitors (GTI) is to identify a small number of defective items and inhibitor items in a large set of items. A test on a subset of items is positive if it satisfies some specific property. Inhibitor items cancel the effects of positive items, which often make the outcome of a test containing defective items negative. Different GTI models can be formulated by considering how specific properties have different cancellation effects. This work introduces generalized GTI (GGTI) in which a new type of items is added, i.e., hybrid items. A hybrid item plays the roles of both defectives items and inhibitor items. Since the number of GGTI models is large (at least 96), we introduce a framework for classifying all types of items non-adaptively, i.e., all tests are designed in advanced. We then explain how GGTI can be used to classify neurons in neuroscience. Finally, we optimize the construction of disjunct matrices, which are an important tool in GTI.

Includes members:
  Member "improved encoding and decoding for non adaptive threshold group testing":
    also connected through other hyperedges:
      (none)
  Member "sublinear decoding schemes for non adaptive group testing with inhibitors":
    also connected through other hyperedges:
      (none)
  Member "efficient nonrandom construction and decoding of non adaptive group testing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "serial quantization for sparse time sequences")
  Member "computational tradeoffs in biological neural networks self stabilizing winner take all networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "winner take all computation in spiking neural networks")
  Member "non adaptive group testing with inhibitors":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sublinear decoding schemes for non adaptive group testing with inhibitors")
  Member "near optimal stochastic threshold group testing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "asymptotics of fingerprinting and group testing tight bounds from channel capacities")
  Member "improved constructions for non adaptive threshold group testing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "improved non adaptive algorithms for threshold group testing with a gap")
  Member "graph constrained group testing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "vertex connectivity measures for node failure identification in boolean network tomography")

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)