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: "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.

Connected through hyperedge HE-1 (induced by source paper "improved non adaptive algorithms for threshold group testing with a gap"):
  with members:
    - "group testing an information theory perspective"
    - "separable codes for the symmetric multiple access channel"
    - "efficiently decodable non adaptive threshold group testing"
    - "superimposed codes and threshold group testing"
    - "grotesque noisy group testing quick and efficient"
    - "improved constructions for non adaptive threshold group testing"
    - "noise resilient group testing limitations and constructions"

Connected through hyperedge HE-2 (induced by source paper "improved encoding and decoding for non adaptive threshold group testing"):
  with members:
    - "efficient nonrandom construction and decoding of non adaptive group testing"
    - "separable codes for the symmetric multiple access channel"
    - "superimposed codes and threshold group testing"
    - "grotesque noisy group testing quick and efficient"
    - "improved constructions for non adaptive threshold group testing"
    - "noise resilient group testing limitations and constructions"
    - "explicit non adaptive combinatorial group testing schemes"

Connected through hyperedge HE-3 (induced by source paper "efficient and error tolerant schemes for non adaptive complex group testing and its application in complex disease genetics"):
  with members:
    - "efficient nonrandom construction and decoding of non adaptive group testing"
    - "grotesque noisy group testing quick and efficient"
    - "improved constructions for non adaptive threshold group testing"
    - "noise resilient group testing limitations and constructions"
    - "explicit non adaptive combinatorial group testing schemes"

Connected through hyperedge HE-4 (induced by source paper "sublinear decoding schemes for non adaptive group testing with inhibitors"):
  with members:
    - "efficient nonrandom construction and decoding of non adaptive group testing"
    - "separable codes for the symmetric multiple access channel"
    - "efficiently decodable non adaptive threshold group testing"
    - "non adaptive group testing with inhibitors"
    - "boolean compressed sensing and noisy group testing"
    - "improved constructions for non adaptive threshold group testing"
    - "noise resilient group testing limitations and constructions"

Connected through hyperedge HE-5 (induced by source paper "efficient nonrandom construction and decoding of non adaptive group testing"):
  with members:
    - "efficient decoding schemes for noisy non adaptive group testing when noise depends on number of items in test"
    - "efficiently decodable non adaptive threshold group testing"
    - "saffron a fast efficient and robust framework for group testing based on sparse graph codes"
    - "faster algorithms for multivariate interpolation with multiplicities and simultaneous polynomial approximations"
    - "boolean compressed sensing and noisy group testing"
    - "noise resilient group testing limitations and constructions"
    - "explicit non adaptive combinatorial group testing schemes"

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)