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: "neural models for sequence chunking"
  Abstract: Many natural language understanding (NLU) tasks, such as shallow parsing (i.e., text chunking) and semantic slot filling, require the assignment of representative labels to the meaningful chunks in a sentence. Most of the current deep neural network (DNN) based methods consider these tasks as a sequence labeling problem, in which a word, rather than a chunk, is treated as the basic unit for labeling. These chunks are then inferred by the standard IOB (Inside-Outside-Beginning) labels. In this paper, we propose an alternative approach by investigating the use of DNN for sequence chunking, and propose three neural models so that each chunk can be treated as a complete unit for labeling. Experimental results show that the proposed neural sequence chunking models can achieve start-of-the-art performance on both the text chunking and slot filling tasks.

Connected through hyperedge HE-1 (induced by source paper "hybrid neural tagging model for open relation extraction"):
  with members:
    - "an open world extension to knowledge graph completion models"
    - "graph neural networks with generated parameters for relation extraction"
    - "openki integrating open information extraction and knowledge bases with relation inference"
    - "design challenges and misconceptions in neural sequence labeling"
    - "ordered neurons integrating tree structures into recurrent neural networks"
    - "a survey on open information extraction"
    - "neural open information extraction"
    - "deep active learning for named entity recognition"

Connected through hyperedge HE-2 (induced by source paper "gcdt a global context enhanced deep transition architecture for sequence labeling"):
  with members:
    - "dtmt a novel deep transition architecture for neural machine translation"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semi supervised sequence modeling with cross view training"
    - "neural reranking for named entity recognition"
    - "attention is all you need"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"
    - "semi supervised multitask learning for sequence labeling"

Connected through hyperedge HE-3 (induced by source paper "enhancing neural sequence labeling with position aware self attention"):
  with members:
    - "self attention with relative position representations"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "deep semantic role labeling with self attention"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"
    - "does higher order lstm have better accuracy for segmenting and labeling sequence data"
    - "semi supervised multitask learning for sequence labeling"

Connected through hyperedge HE-4 (induced by source paper "supervised neural models revitalize the open relation extraction"):
  with members:
    - "neural open information extraction"
    - "deep active learning for named entity recognition"
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "joint extraction of entities and relations based on a novel tagging scheme"
    - "crowdsourcing question answer meaning representations"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "unsupervised pretraining for sequence to sequence learning"
    - "neural architectures for named entity recognition"

Connected through hyperedge HE-5 (induced by source paper "a survey on deep learning for named entity recognition"):
  with members:
    - "learning a unified named entity tagger from multiple partially annotated corpora for efficient adaptation"
    - "a unified mrc framework for named entity recognition"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "a little annotation does a lot of good a study in bootstrapping low resource named entity recognizers"
    - "dependency guided lstm crf for named entity recognition"
    - "hierarchical contextualized representation for named entity recognition"
    - "dice loss for data imbalanced nlp tasks"
    - "attending to entities for better text understanding"

Connected through hyperedge HE-6 (induced by source paper "design challenges and misconceptions in neural sequence labeling"):
  with members:
    - "neural reranking for named entity recognition"
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"
    - "semi supervised multitask learning for sequence labeling"
    - "semi supervised sequence tagging with bidirectional language models"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "optimal hyperparameters for deep lstm networks for sequence labeling tasks"
    - "empower sequence labeling with task aware neural language model"

Connected through hyperedge HE-7 (induced by source paper "neural crf transducers for sequence labeling"):
  with members:
    - "deep contextualized word representations"
    - "learning approximate inference networks for structured prediction"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"
    - "does higher order lstm have better accuracy for segmenting and labeling sequence data"
    - "semi supervised sequence tagging with bidirectional language models"
    - "empower sequence labeling with task aware neural language model"
    - "sequence to sequence learning as beam search optimization"
    - "end to end sequence labeling via bi directional lstm cnns crf"

Connected through hyperedge HE-8 (induced by source paper "sequence labeling a practical approach"):
  with members:
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "semi supervised sequence tagging with bidirectional language models"
    - "robust multilingual named entity recognition with shallow semi supervised features"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "multi task cross lingual sequence tagging from scratch"
    - "neural architectures for named entity recognition"
    - "multilingual language processing from bytes"
    - "finding function in form compositional character models for open vocabulary word representation"

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)