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: "contextualizing citations for scientific summarization using word embeddings and domain knowledge"
  Abstract: Citation texts are sometimes not very informative or in some cases inaccurate by themselves; they need the appropriate context from the referenced paper to reflect its exact contributions. To address this problem, we propose an unsupervised model that uses distributed representation of words as well as domain knowledge to extract the appropriate context from the reference paper. Evaluation results show the effectiveness of our model by significantly outperforming the state-of-the-art. We furthermore demonstrate how an effective contextualization method results in improving citation-based summarization of the scientific articles.

Connected through hyperedge HE-1 (induced by source paper "citation text generation"):
  with members:
    - "structural scaffolds for citation intent classification in scientific publications"
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "scisummnet a large annotated corpus and content impact models for scientific paper summarization with citation networks"
    - "bertscore evaluating text generation with bert"
    - "defending against neural fake news"
    - "gorc a large contextual citation graph of academic papers"
    - "superglue a stickier benchmark for general purpose language understanding systems"
    - "content based citation recommendation"

Connected through hyperedge HE-2 (induced by source paper "scisummnet a large annotated corpus and content impact models for scientific paper summarization with citation networks"):
  with members:
    - "the cl scisumm shared task 2018 results and key insights"
    - "scientific document summarization via citation contextualization and scientific discourse"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "a discourse aware attention model for abstractive summarization of long documents"
    - "a supervised approach to extractive summarisation of scientific papers"
    - "get to the point summarization with pointer generator networks"
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "scientific article summarization using citation context and article s discourse structure"

Connected through hyperedge HE-3 (induced by source paper "structural scaffolds for citation intent classification in scientific publications"):
  with members:
    - "scibert pretrained contextualized embeddings for scientific text"
    - "syntactic scaffolds for semantic structures"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "neural multi task learning for citation function and provenance"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "scientific article summarization using citation context and article s discourse structure"

Connected through hyperedge HE-4 (induced by source paper "scientific document summarization via citation contextualization and scientific discourse"):
  with members:
    - "scientific article summarization using citation context and article s discourse structure"
    - "all fingers are not equal intensity of references in scientific articles"
    - "counter fitting word vectors to linguistic constraints"
    - "learning based single document summarization with compression and anaphoricity constraints"
    - "on the composition of scientific abstracts"
    - "citation classification for behavioral analysis of a scientific field"
    - "a neural attention model for abstractive sentence summarization"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"

Connected through hyperedge HE-5 (induced by source paper "a discourse aware attention model for abstractive summarization of long documents"):
  with members:
    - "attention is all you need"
    - "get to the point summarization with pointer generator networks"
    - "generating high quality and informative conversation responses with sequence to sequence models"
    - "challenges in data to document generation"
    - "a deep reinforced model for abstractive summarization"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "language as a latent variable discrete generative models for sentence compression"
    - "sequence to sequence rnns for text summarization"

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)