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: "semeval 2017 task 10 scienceie extracting keyphrases and relations from scientific publications"
  Abstract: We describe the SemEval task of extracting keyphrases and relations between them from scientific documents, which is crucial for understanding which publications describe which processes, tasks and materials. Although this was a new task, we had a total of 26 submissions across 3 evaluation scenarios. We expect the task and the findings reported in this paper to be relevant for researchers working on understanding scientific content, as well as the broader knowledge base population and information extraction communities.

Connected through hyperedge HE-1 (induced by source paper "domain independent extraction of scientific concepts from research articles"):
  with members:
    - "structural scaffolds for citation intent classification in scientific publications"
    - "investigating correlations of inter coder agreement and machine annotation performance for historical video data"
    - "hierarchical neural networks for sequential sentence classification in medical scientific abstracts"
    - "multi task identification of entities relations and coreference for scientific knowledge graph construction"
    - "allennlp a deep semantic natural language processing platform"
    - "construction of the literature graph in semantic scholar"
    - "deep bayesian active learning for natural language processing results of a large scale empirical study"
    - "end to end neural coreference resolution"

Connected through hyperedge HE-2 (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"
    - "scientific information extraction with semi supervised neural tagging"
    - "end to end sequence labeling via bi directional lstm cnns crf"

Connected through hyperedge HE-3 (induced by source paper "a review of keyphrase extraction"):
  with members:
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "keyphrase generation with correlation constraints"
    - "learning feature representations for keyphrase extraction"
    - "semi supervised learning for neural keyphrase generation"
    - "simple unsupervised keyphrase extraction using sentence embeddings"
    - "joint entity recognition and relation extraction as a multi head selection problem"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "unsupervised learning of sentence embeddings using compositional n gram features"

Connected through hyperedge HE-4 (induced by source paper "using similarity measures to select pretraining data for ner"):
  with members:
    - "an embarrassingly simple approach for transfer learning from pretrained language models"
    - "design challenges and misconceptions in neural sequence labeling"
    - "deep contextualized word representations"
    - "an annotated corpus for machine reading of instructions in wet lab protocols"
    - "what s in a domain learning domain robust text representations using adversarial training"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "uncovering divergent linguistic information in word embeddings with lessons for intrinsic and extrinsic evaluation"
    - "advances in pre training distributed word representations"

Connected through hyperedge HE-5 (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"
    - "scientific information extraction with semi supervised neural tagging"
    - "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-6 (induced by source paper "conflict and cooperation ai research and development in terms of the economy of conventions"):
  with members:
    - "identifying experts in software libraries and frameworks among github users"
    - "scibert pretrained contextualized embeddings for scientific text"
    - "was ist eine professur fuer kuenstliche intelligenz"
    - "an explorative study of github repositories of ai papers"
    - "won t we fix this issue qualitative characterization and automated identification of wontfix issues on github"
    - "estimating defectiveness of source code a predictive model using github content"
    - "what s in a github star understanding repository starring practices in a social coding platform"
    - "multi task identification of entities relations and coreference for scientific knowledge graph construction"

Connected through hyperedge HE-7 (induced by source paper "keyphrase extraction from scholarly articles as sequence labeling using contextualized embeddings"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "scibert a pretrained language model for scientific text"
    - "transformer xl attentive language models beyond a fixed length context"
    - "a multiscale visualization of attention in the transformer model"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-8 (induced by source paper "open domain web keyphrase extraction beyond language modeling"):
  with members:
    - "keyphrase generation with correlation constraints"
    - "towards better text understanding and retrieval through kernel entity salience modeling"
    - "deep contextualized word representations"
    - "automatic event salience identification"
    - "semi supervised learning for neural keyphrase generation"
    - "generating diverse numbers of diverse keyphrases"
    - "title guided encoding for keyphrase generation"
    - "attention is all you need"

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)