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: "generating extractive summaries of scientific paradigms"
  Abstract: Researchers and scientists increasingly find themselves in the position of having to quickly understand large amounts of technical material. Our goal is to effectively serve this need by using bibliometric text mining and summarization techniques to generate summaries of scientific literature. We show how we can use citations to produce automatically generated, readily consumable, technical extractive summaries. We first propose C-LexRank, a model for summarizing single scientific articles based on citations, which employs community detection and extracts salient information-rich sentences. Next, we further extend our experiments to summarize a set of papers, which cover the same scienti fic topic. We generate extractive summaries of a set of Question Answering (QA) and Dependency Parsing (DP) papers, their abstracts, and their citation sentences and show that citations have unique information amenable to creating a summary.

Connected through hyperedge HE-1 (induced by source paper "what is this article about extreme summarization with topic aware convolutional neural networks"):
  with members:
    - "fine tune bert for extractive summarization"
    - "jointly extracting and compressing documents with summary state representations"
    - "scisummnet a large annotated corpus and content impact models for scientific paper summarization with citation networks"
    - "mass masked sequence to sequence pre training for language generation"
    - "generating summaries with topic templates and structured convolutional decoders"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "unified language model pre training for natural language understanding and generation"
    - "deep communicating agents for abstractive summarization"

Connected through hyperedge HE-2 (induced by source paper "controlling the amount of verbatim copying in abstractive summarization"):
  with members:
    - "sample efficient text summarization using a single pre trained transformer"
    - "biset bi directional selective encoding with template for abstractive summarization"
    - "bertscore evaluating text generation with bert"
    - "unified language model pre training for natural language understanding and generation"
    - "deep communicating agents for abstractive summarization"
    - "improving abstraction in text summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "bottom up abstractive summarization"

Connected through hyperedge HE-3 (induced by source paper "the state of nlp literature a diachronic analysis of the acl anthology"):
  with members:
    - "google scholar web of science and scopus a systematic comparison of citations in 252 subject categories"
    - "measuring academic influence not all citations are equal"
    - "about the size of google scholar playing the numbers"

Connected through hyperedge HE-4 (induced by source paper "a novel ilp framework for summarizing content with high lexical variety"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "scientific document summarization via citation contextualization and scientific discourse"
    - "toward extractive summarization of online forum discussions via hierarchical attention networks"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "soft layer specific multi task summarization with entailment and question generation"
    - "automatic summarization of student course feedback"
    - "an improved phrase based approach to annotating and summarizing student course responses"

Connected through hyperedge HE-5 (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-6 (induced by source paper "identifying reference spans topic modeling and word embeddings help ir"):
  with members:
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-7 (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"
    - "scientific paper summarization using citation summary networks"

Connected through hyperedge HE-8 (induced by source paper "scientific article summarization using citation context and article s discourse structure"):
  with members:
    - "a novel feature based bayesian model for query focused multi document summarization"
    - "scientific paper summarization using citation summary networks"
    - "lexrank graph based lexical centrality as salience in 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.IR(Information Retrieval)