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: "explaining deep neural networks using spectrum based fault localization"
  Abstract: Deep neural networks (DNNs) increasingly replace traditionally developed software in a broad range of applications. However, in stark contrast to traditional software, the black-box nature of DNNs makes it impossible to understand their outputs, creating demand for "Explainable AI". Explanations of the outputs of the DNN are essential for the training process and are supporting evidence of the adequacy of the DNN. In this paper, we show that spectrum-based fault localization delivers good explanations of the outputs of DNNs. We present an algorithm and a tool PROTOZOA, which synthesizes a ranking of the parts of the inputs using several spectrum-based fault localization measures. We show that the highest-ranked parts provide explanations that are consistent with the standard definitions of explanations in the literature. Our experimental results on ImageNet show that the explanations we generate are useful visual indicators for the progress of the training of the DNN. We compare the results of PROTOZOA with SHAP and show that the explanations generated by PROTOZOA are on par or superior. We also generate adversarial examples using our explanations; the efficiency of this process can serve as a proxy metric for the quality of the explanations. Our measurements show that PROTOZOA's explanations yield a higher number of adversarial examples than those produced by SHAP.

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.LG(Machine Learning)