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 open information extraction"
  Abstract: Conventional Open Information Extraction (Open IE) systems are usually built on hand-crafted patterns from other NLP tools such as syntactic parsing, yet they face problems of error propagation. In this paper, we propose a neural Open IE approach with an encoder-decoder framework. Distinct from existing methods, the neural Open IE approach learns highly confident arguments and relation tuples bootstrapped from a state-of-the-art Open IE system. An empirical study on a large benchmark dataset shows that the neural Open IE system significantly outperforms several baselines, while maintaining comparable computational efficiency.

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"
    - "deep active learning for named entity recognition"
    - "neural models for sequence chunking"

Connected through hyperedge HE-2 (induced by source paper "effective modeling of encoder decoder architecture for joint entity and relation extraction"):
  with members:
    - "distant supervision relation extraction with intra bag and inter bag attentions"
    - "effective attention modeling for neural relation extraction"
    - "end to end neural relation extraction using deep biaffine attention"
    - "attention guided graph convolutional networks for relation extraction"
    - "a question focused multi factor attention network for question answering"
    - "improving distantly supervised relation extraction using word and entity based attention"
    - "reside improving distantly supervised neural relation extraction using side information"
    - "deep graph convolutional encoders for structured data to text generation"

Connected through hyperedge HE-3 (induced by source paper "open information extraction from question answer pairs"):
  with members:
    - "transforming question answering datasets into natural language inference datasets"
    - "opennmt open source toolkit for neural machine translation"
    - "indirect supervision for relation extraction using question answer pairs"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "learning a neural semantic parser from user feedback"
    - "a syntactic neural model for general purpose code generation"
    - "a constrained sequence to sequence neural model for sentence simplification"
    - "recurrent neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "supervised neural models revitalize the open relation extraction"):
  with members:
    - "deep active learning for named entity recognition"
    - "neural models for sequence chunking"
    - "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 "span model for open information extraction on accurate corpus"):
  with members:
    - "global greedy dependency parsing"
    - "dependency or span end to end uniform semantic role labeling"
    - "head driven phrase structure grammar parsing on penn treebank"
    - "graphene a context preserving open information extraction system"
    - "a full end to end semantic role labeler syntax agnostic over syntax aware"
    - "a span selection model for semantic role labeling"
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "end to end neural coreference resolution"

Connected through hyperedge HE-6 (induced by source paper "comet commonsense transformers for automatic knowledge graph construction"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "attention is all you need"
    - "zero shot relation extraction via reading comprehension"
    - "conceptnet 5 5 an open multilingual graph of general knowledge"
    - "enriching word vectors with subword information"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-7 (induced by source paper "learning open information extraction of implicit relations from reading comprehension datasets"):
  with members:
    - "transforming question answering datasets into natural language inference datasets"
    - "a survey on open information extraction"
    - "opennmt open source toolkit for neural machine translation"
    - "newsqa a machine comprehension dataset"
    - "squad 100 000 questions for machine comprehension of text"
    - "getting more out of syntax with props"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-8 (induced by source paper "improving open information extraction via iterative rank aware learning"):
  with members:
    - "supervised neural models revitalize the open relation extraction"
    - "logician a unified end to end neural approach for open domain information extraction"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "getting more out of syntax with props"
    - "training very deep networks"
    - "adadelta an adaptive learning rate method"

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)