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: "a survey on open information extraction"
  Abstract: We provide a detailed overview of the various approaches that were proposed to date to solve the task of Open Information Extraction. We present the major challenges that such systems face, show the evolution of the suggested approaches over time and depict the specific issues they address. In addition, we provide a critique of the commonly applied evaluation procedures for assessing the performance of Open IE systems and highlight some directions for future work.

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

Connected through hyperedge HE-2 (induced by source paper "generalizing natural language analysis through span relation representations"):
  with members:
    - "enriching pre trained language model with entity information for relation classification"
    - "bert rediscovers the classical nlp pipeline"
    - "spanbert improving pre training by representing and predicting spans"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "multi task deep neural networks for natural language understanding"
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "simple bert models for relation extraction and semantic role labeling"
    - "bert post training for review reading comprehension and aspect based sentiment analysis"

Connected through hyperedge HE-3 (induced by source paper "differentiable reasoning on large knowledge bases and natural language"):
  with members:
    - "nlprolog reasoning with weak unification for question answering in natural language"
    - "towards neural theorem proving at scale"
    - "learning explanatory rules from noisy data"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "kblrn end to end learning of knowledge base representations with latent relational and numerical features"
    - "billion scale similarity search with gpus"
    - "end to end differentiable proving"
    - "go for a walk and arrive at the answer reasoning over paths in knowledge bases using reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "identifying supporting facts for multi hop question answering with document graph networks"):
  with members:
    - "transforming complex sentences into a semantic hierarchy"
    - "graph neural networks a review of methods and applications"
    - "bilinear attention networks"
    - "graphene semantically linked propositions in open information extraction"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "learning to search in long documents using document structure"
    - "know what you don t know unanswerable questions for squad"
    - "relational inductive biases deep learning and graph networks"

Connected through hyperedge HE-5 (induced by source paper "nlprolog reasoning with weak unification for question answering in natural language"):
  with members:
    - "coarse grain fine grain coattention network for multi evidence question answering"
    - "weaver deep co encoding of questions and documents for machine reading"
    - "dynamic integration of background knowledge in neural nlu systems"
    - "neural models for reasoning over multiple mentions using coreference"
    - "question answering by reasoning across documents with graph convolutional networks"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "learning explanatory rules from noisy data"
    - "neural symbolic learning and reasoning a survey and interpretation"

Connected through hyperedge HE-6 (induced by source paper "named entity recognition is there a glass ceiling"):
  with members:
    - "graph convolution for multimodal information extraction from visually rich documents"
    - "a survey on recent advances in named entity recognition from deep learning models"
    - "a survey on deep learning for named entity recognition"
    - "chargrid towards understanding 2d documents"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semi supervised sequence modeling with cross view training"
    - "snorkel rapid training data creation with weak supervision"

Connected through hyperedge HE-7 (induced by source paper "unsupervised text generation from structured data"):
  with members:
    - "text generation from knowledge graphs with graph transformers"
    - "generating fine grained open vocabulary entity type descriptions"
    - "phrase based neural unsupervised machine translation"
    - "data to text generation with content selection and planning"
    - "massive exploration of neural machine translation architectures"
    - "challenges in data to document generation"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "visual relationship detection with language priors"

Connected through hyperedge HE-8 (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"
    - "neural 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"

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)