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: "zero shot sequence labeling transferring knowledge from sentences to tokens"
  Abstract: Can attention- or gradient-based visualization techniques be used to infer token-level labels for binary sequence tagging problems, using networks trained only on sentence-level labels? We construct a neural network architecture based on soft attention, train it as a binary sentence classifier and evaluate against token-level annotation on four different datasets. Inferring token labels from a network provides a method for quantitatively evaluating what the model is learning, along with generating useful feedback in assistance systems. Our results indicate that attention-based methods are able to predict token-level labels more accurately, compared to gradient-based methods, sometimes even rivaling the supervised oracle network.

Connected through hyperedge HE-1 (induced by source paper "sequence labeling parsing by learning across representations"):
  with members:
    - "viable dependency parsing as sequence labeling"
    - "better faster stronger sequence tagging constituent parsers"
    - "left to right dependency parsing with pointer networks"
    - "multitask parsing across semantic representations"
    - "constituent parsing as sequence labeling"
    - "ncrf an open source neural sequence labeling toolkit"
    - "constituency parsing with a self attentive encoder"
    - "identifying beneficial task relations for multi task learning in deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "label agnostic sequence labeling by copying nearest neighbors"):
  with members:
    - "retrieve and refine improved sequence generation models for dialogue"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "generating sentences by editing prototypes"
    - "attention is all you need"
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "learned in translation contextualized word vectors"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"

Connected through hyperedge HE-3 (induced by source paper "weakly supervised attention networks for fine grained opinion mining and public health"):
  with members:
    - "attention based deep multiple instance learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deriving machine attention from human rationales"
    - "structured attention networks"
    - "multiple instance learning a survey of problem characteristics and applications"
    - "multiple instance learning networks for fine grained sentiment analysis"
    - "revisiting recurrent networks for paraphrastic sentence embeddings"
    - "neural attention models for sequence classification analysis and application to key term extraction and dialogue act detection"

Connected through hyperedge HE-4 (induced by source paper "ranking and selecting multi hop knowledge paths to better predict human needs"):
  with members:
    - "modeling naive psychology of characters in simple commonsense stories"
    - "deep learning for sentiment analysis a survey"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "dynamic integration of background knowledge in neural nlu systems"
    - "deep contextualized word representations"
    - "commonsense for generative multi hop question answering tasks"
    - "reflections on sentiment opinion analysis"
    - "simulating action dynamics with neural process networks"

Connected through hyperedge HE-5 (induced by source paper "generating token level explanations for natural language inference"):
  with members:
    - "attention is not explanation"
    - "pair2vec compositional word pair embeddings for cross sentence inference"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "e snli natural language inference with natural language explanations"
    - "neural network models for paraphrase identification semantic textual similarity natural language inference and question answering"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "a decomposable attention model for natural language inference"

Connected through hyperedge HE-6 (induced by source paper "area attention"):
  with members:
    - "end to end neural coreference resolution"
    - "attention is all you need"
    - "areas of attention for image captioning"
    - "image captioning with semantic attention"
    - "neural attention models for sequence classification analysis and application to key term extraction and dialogue act detection"
    - "neural machine translation in linear time"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "benchmarking zero shot text classification datasets evaluation and entailment approach"):
  with members:
    - "integrating semantic knowledge to tackle zero shot text classification"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "fever a large scale dataset for fact extraction and verification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "zero shot user intent detection via capsule neural networks"
    - "train once test anywhere zero shot learning for text classification"
    - "zero shot relation extraction via reading comprehension"
    - "a broad coverage challenge corpus for sentence understanding through inference"

Connected through hyperedge HE-8 (induced by source paper "detecting local insights from global labels supervised zero shot sequence labeling via a convolutional decomposition"):
  with members:
    - "context is key grammatical error detection with contextual word representations"
    - "attention is not explanation"
    - "understanding convolutional neural networks for text classification"
    - "jointly learning to label sentences and tokens"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semi supervised multitask learning for sequence labeling"
    - "understanding state preferences with text as data introducing the un general debate corpus"
    - "google s neural machine translation system bridging the gap between human and machine translation"

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)