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 paper summarization using citation summary networks"
  Abstract: Quickly moving to a new area of research is painful for researchers due to the vast amount of scientific literature in each field of study. One possible way to overcome this problem is to summarize a scientific topic. In this paper, we propose a model of summarizing a single article, which can be further used to summarize an entire topic. Our model is based on analyzing others' viewpoint of the target article's contributions and the study of its citation summary network using a clustering approach.

Connected through hyperedge HE-1 (induced by source paper "a study of human summaries of scientific articles"):
  with members:
    - "a summarization system for scientific documents"
    - "shortscience org reproducing intuition"

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"
    - "contextualizing citations for scientific summarization using word embeddings and domain knowledge"
    - "get to the point summarization with pointer generator networks"
    - "encoding sentences with graph convolutional networks for semantic role labeling"

Connected through hyperedge HE-3 (induced by source paper "gorc a large contextual citation graph of academic papers"):
  with members:
    - "structural scaffolds for citation intent classification in scientific publications"
    - "a scalable hybrid research paper recommender system for microsoft academic"
    - "a context aware citation recommendation model with bert and graph convolutional networks"
    - "roberta a robustly optimized bert pretraining approach"
    - "content based citation recommendation"
    - "deep contextualized word representations"
    - "a dataset of peer reviews peerread collection insights and nlp applications"
    - "construction of the literature graph in semantic scholar"

Connected through hyperedge HE-4 (induced by source paper "a summarization system for scientific documents"):
  with members:
    - "scisummnet a large annotated corpus and content impact models for scientific paper summarization with citation networks"
    - "identification of tasks datasets evaluation metrics and numeric scores for scientific leaderboards construction"
    - "multi task identification of entities relations and coreference for scientific knowledge graph construction"
    - "tutorialbank a manually collected corpus for prerequisite chains survey extraction and resource recommendation"
    - "cl scholar the acl anthology knowledge graph miner"

Connected through hyperedge HE-5 (induced by source paper "overview and results cl scisumm shared task 2019"):
  with members:
    - "scisummnet a large annotated corpus and content impact models for scientific paper summarization with citation networks"

Connected through hyperedge HE-6 (induced by source paper "scientific document summarization via citation contextualization and scientific discourse"):
  with members:
    - "contextualizing citations for scientific summarization using word embeddings and domain knowledge"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "contextualizing citations for scientific summarization using word embeddings and domain knowledge"):
  with members:
    - "scientific document summarization via citation contextualization and scientific discourse"
    - "classify or select neural architectures for extractive document summarization"
    - "scientific article summarization using citation context and article s discourse structure"
    - "a dual embedding space model for document ranking"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "retrofitting word vectors to semantic lexicons"

Connected through hyperedge HE-8 (induced by source paper "the closer the better similarity of publication pairs at different co citation levels"):
  with members:
    - "which type of citation analysis generates the most accurate taxonomy of scientific and technical knowledge"
    - "context sensitive article ranking with citation context analysis"
    - "generating extractive summaries of scientific paradigms"

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.IR(Information Retrieval)