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 "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.

Includes members:
  Member "context is key grammatical error detection with contextual word representations":
    also connected through other hyperedges:
      (none)
  Member "attention is not explanation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "counterfactual samples synthesizing for robust visual question answering")
  Member "understanding convolutional neural networks for text classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "lucid a practical lightweight deep learning solution for ddos attack detection")
  Member "jointly learning to label sentences and tokens":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "linguistic knowledge and transferability of contextual representations")
  Member "zero shot sequence labeling transferring knowledge from sentences to tokens":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sequence labeling parsing by learning across representations")
  Member "bert pre training of deep bidirectional transformers for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "semi supervised multitask learning for sequence labeling":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "exploring self supervised regularization for supervised and semi supervised learning")
  Member "understanding state preferences with text as data introducing the un general debate corpus":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the dynamic embedded topic model")

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.CL(Computation and Language)