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: "detecting local insights from global labels supervised zero shot sequence labeling via a convolutional decomposition"
  Abstract: Zero-shot grammatical error detection is the task of tagging token-level errors in a sentence when only given access to labels at the sentence-level for training. We present and analyze BLADE, a sequence labeling approach based on a decomposition of the filter-ngram interactions of a single-layer one-dimensional convolutional neural network as the final layer of a network, which has the characteristic of being effective in both the fully-supervised and zero-shot settings. The approach also enables a matching method, exemplar auditing, useful for analyzing the model and data, and empirically, as part of an inference-time decision rule. Additionally, we extend these insights from natural language to machine generated language, demonstrating that the strong sequence model can be used to guide synthetic text generation, and that it is concomitantly unsuitable as a reliable detector of synthetic data when the detection model and a sufficiently strong generation model are both accessible. We close with qualitative evidence that the approach can be a useful tool for preliminary text and document analysis, demonstrating that a strong text feature extractor for low- and high- resource settings is useful across NLP tasks.

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.CL(Computation and Language)