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: "scientific information extraction with semi supervised neural tagging"
  Abstract: This paper addresses the problem of extracting keyphrases from scientific articles and categorizing them as corresponding to a task, process, or material. We cast the problem as sequence tagging and introduce semi-supervised methods to a neural tagging model, which builds on recent advances in named entity recognition. Since annotated training data is scarce in this domain, we introduce a graph-based semi-supervised algorithm together with a data selection scheme to leverage unannotated articles. Both inductive and transductive semi-supervised learning strategies outperform state-of-the-art information extraction performance on the 2017 SemEval Task 10 ScienceIE task.

Connected through hyperedge HE-1 (induced by source paper "the stem ecr dataset grounding scientific entity references in stem scholarly content to authoritative encyclopedic and lexicographic sources"):
  with members:
    - "scibert a pretrained language model for scientific text"
    - "open research knowledge graph next generation infrastructure for semantic scholarly knowledge"
    - "multi task identification of entities relations and coreference for scientific knowledge graph construction"
    - "allennlp a deep semantic natural language processing platform"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "construction of the literature graph in semantic scholar"
    - "semeval 2017 task 10 scienceie extracting keyphrases and relations from scientific publications"
    - "end to end sequence labeling via bi directional lstm cnns crf"

Connected through hyperedge HE-2 (induced by source paper "neural keyphrase generation via reinforcement learning with adaptive rewards"):
  with members:
    - "an integrated approach for keyphrase generation via exploring the power of retrieval and extraction"
    - "microblog hashtag generation via encoding conversation contexts"
    - "keyphrase generation with correlation constraints"
    - "a reinforced topic aware convolutional sequence to sequence model for abstractive text summarization"
    - "semi supervised learning for neural keyphrase generation"
    - "gaan gated attention networks for learning on large and spatiotemporal graphs"
    - "multi head attention with disagreement regularization"
    - "generating diverse numbers of diverse keyphrases"

Connected through hyperedge HE-3 (induced by source paper "information extraction from scientific literature for method recommendation"):
  with members:
    - "multi task identification of entities relations and coreference for scientific knowledge graph construction"
    - "knowledge graph completion via complex tensor factorization"
    - "graph based semi supervised conditional random fields for spoken language understanding using unaligned data"
    - "semeval 2017 task 10 scienceie extracting keyphrases and relations from scientific publications"
    - "multi task learning for speaker role adaptation in neural conversation models"
    - "distant supervision for relation extraction beyond the sentence boundary"
    - "lstm based conversation models"
    - "end to end sequence labeling via bi directional lstm cnns crf"

Connected through hyperedge HE-4 (induced by source paper "a general framework for information extraction using dynamic span graphs"):
  with members:
    - "a walk based model on entity graphs for relation extraction"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "multi task identification of entities relations and coreference for scientific knowledge graph construction"
    - "neural segmental hypergraphs for overlapping mention recognition"
    - "a hierarchical multi task approach for learning embeddings from semantic tasks"
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "deep contextualized word representations"
    - "an annotated corpus for machine reading of instructions in wet lab protocols"

Connected through hyperedge HE-5 (induced by source paper "an integrated approach for keyphrase generation via exploring the power of retrieval and extraction"):
  with members:
    - "keyphrase generation with correlation constraints"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "semi supervised learning for neural keyphrase generation"
    - "generating diverse numbers of diverse keyphrases"
    - "title guided encoding for keyphrase generation"
    - "opennmt open source toolkit for neural machine translation"
    - "deep keyphrase generation"
    - "get to the point summarization with pointer generator networks"

Connected through hyperedge HE-6 (induced by source paper "does order matter an empirical study on generating multiple keyphrases as a sequence"):
  with members:
    - "keyphrase generation a text summarization struggle"
    - "divgraphpointer a graph pointer network for extracting diverse keyphrases"
    - "keyphrase generation with correlation constraints"
    - "bottom up abstractive summarization"
    - "one size does not fit all generating and evaluating variable number of keyphrases"
    - "semi supervised learning for neural keyphrase generation"
    - "the importance of generation order in language modeling"
    - "neural models for key phrase detection and question generation"

Connected through hyperedge HE-7 (induced by source paper "identification of tasks datasets evaluation metrics and numeric scores for scientific leaderboards construction"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "multi task identification of entities relations and coreference for scientific knowledge graph construction"
    - "collecting diverse natural language inference problems for sentence representation evaluation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semantic sentence matching with densely connected recurrent and co attentive information"
    - "construction of the literature graph in semantic scholar"
    - "attention is all you need"
    - "semeval 2017 task 10 scienceie extracting keyphrases and relations from scientific publications"

Connected through hyperedge HE-8 (induced by source paper "extracting factual min max age information from clinical trial studies"):
  with members:
    - "clicr a dataset of clinical case reports for machine reading comprehension"
    - "bidirectional attention flow for machine comprehension"
    - "squad 100 000 questions for machine comprehension of text"

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)