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: "learning immune defectives graph through group tests"
  Abstract: This paper deals with an abstraction of a unified problem of drug discovery and pathogen identification. Pathogen identification involves identification of disease-causing biomolecules. Drug discovery involves finding chemical compounds, called lead compounds, that bind to pathogenic proteins and eventually inhibit the function of the protein. In this paper, the lead compounds are abstracted as inhibitors, pathogenic proteins as defectives, and the mixture of "ineffective" chemical compounds and non-pathogenic proteins as normal items. A defective could be immune to the presence of an inhibitor in a test. So, a test containing a defective is positive iff it does not contain its "associated" inhibitor. The goal of this paper is to identify the defectives, inhibitors, and their "associations" with high probability, or in other words, learn the Immune Defectives Graph (IDG) efficiently through group tests. We propose a probabilistic non-adaptive pooling design, a probabilistic two-stage adaptive pooling design and decoding algorithms for learning the IDG. For the two-stage adaptive-pooling design, we show that the sample complexity of the number of tests required to guarantee recovery of the inhibitors, defectives, and their associations with high probability, i.e., the upper bound, exceeds the proposed lower bound by a logarithmic multiplicative factor in the number of items. For the non-adaptive pooling design too, we show that the upper bound exceeds the proposed lower bound by at most a logarithmic multiplicative factor in the number of items.

Connected through hyperedge HE-1 (induced by source paper "on the optimality of the kautz singleton construction in probabilistic group testing"):
  with members:
    - "performance of group testing algorithms with near constant tests per item"
    - "noisy non adaptive group testing a near definite defectives approach"
    - "near optimal noisy group testing via separate decoding of items"
    - "nearly optimal sparse group testing"
    - "nonadaptive group testing with random set of defectives"
    - "saffron a fast efficient and robust framework for group testing based on sparse graph codes"
    - "group testing algorithms bounds and simulations"
    - "non adaptive group testing explicit bounds and novel algorithms"

Connected through hyperedge HE-2 (induced by source paper "sparse combinatorial group testing for low energy massive random access"):
  with members:
    - "on the optimality of the kautz singleton construction in probabilistic group testing"
    - "nearly optimal sparse group testing"
    - "low power wide area networks an overview"
    - "capacity of the energy harvesting gaussian mac"
    - "saffron a fast efficient and robust framework for group testing based on sparse graph codes"
    - "non adaptive probabilistic group testing with noisy measurements near optimal bounds with efficient algorithms"
    - "note on noisy group testing asymptotic bounds and belief propagation reconstruction"
    - "explicit non adaptive combinatorial group testing schemes"

Connected through hyperedge HE-3 (induced by source paper "sparse combinatorial group testing"):
  with members:
    - "nearly optimal sparse group testing"
    - "capacity of the energy harvesting gaussian mac"
    - "saffron a fast efficient and robust framework for group testing based on sparse graph codes"
    - "noise resilient group testing limitations and constructions"
    - "explicit non adaptive combinatorial group testing schemes"

Connected through hyperedge HE-4 (induced by source paper "saffron a fast efficient and robust framework for group testing based on sparse graph codes"):
  with members:
    - "nonadaptive group testing with random set of defectives via constant weight codes"
    - "computing a k sparse n length discrete fourier transform using at most 4k samples and o k log k complexity"
    - "grotesque noisy group testing quick and efficient"
    - "non adaptive group testing explicit bounds and novel algorithms"
    - "boolean compressed sensing and noisy group testing"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"

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)