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: "bayesian query focused summarization"
  Abstract: We present BayeSum (for ``Bayesian summarization''), a model for sentence extraction in query-focused summarization. BayeSum leverages the common case in which multiple documents are relevant to a single query. Using these documents as reinforcement for query terms, BayeSum is not afflicted by the paucity of information in short queries. We show that approximate inference in BayeSum is possible on large data sets and results in a state-of-the-art summarization system. Furthermore, we show how BayeSum can be understood as a justified query expansion technique in the language modeling for IR framework.

Connected through hyperedge HE-1 (induced by source paper "improving the similarity measure of determinantal point processes for extractive multi document summarization"):
  with members:
    - "parabank monolingual bitext generation and sentential paraphrasing via lexically constrained neural machine translation"
    - "scoring sentence singletons and pairs for abstractive summarization"
    - "deep communicating agents for abstractive summarization"
    - "improving abstraction in text summarization"
    - "content selection in deep learning models of summarization"
    - "bottom up abstractive summarization"
    - "toward extractive summarization of online forum discussions via hierarchical attention networks"
    - "ranking sentences for extractive summarization with reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "automatic text summarization what has been done and what has to be done"):
  with members:
    - "toward abstractive summarization using semantic representations"
    - "learning based single document summarization with compression and anaphoricity constraints"
    - "a neural attention model for abstractive sentence summarization"
    - "extraction of salient sentences from labelled documents"
    - "on using very large target vocabulary for neural machine translation"
    - "lexrank graph based lexical centrality as salience in text summarization"
    - "inferring strategies for sentence ordering in multidocument news summarization"
    - "applying natural language generation to indicative summarization"

Connected through hyperedge HE-3 (induced by source paper "reinforced extractive summarization with question focused rewards"):
  with members:
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "structure infused copy mechanisms for abstractive summarization"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "get to the point summarization with pointer generator networks"
    - "adversarial learning for neural dialogue generation"
    - "faithful to the original fact aware neural abstractive summarization"
    - "a deep reinforced model for abstractive summarization"
    - "a thorough examination of the cnn daily mail reading comprehension task"

Connected through hyperedge HE-4 (induced by source paper "a joint model of conversational discourse and latent topics on microblogs"):
  with members:
    - "improving topic models with latent feature word representations"
    - "joint modeling of content and discourse relations in dialogues"
    - "jointly learning word embeddings and latent topics"
    - "tweet2vec character based distributed representations for social media"
    - "learning reporting dynamics during breaking news for rumour detection in social media"
    - "skip thought vectors"
    - "unsupervised cross domain word representation learning"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-5 (induced by source paper "query focused abstractive summarization incorporating query relevance multi document coverage and summary length constraints into seq2seq models"):
  with members:
    - "diversity driven attention model for query based abstractive summarization"
    - "get to the point summarization with pointer generator networks"
    - "sequence to sequence rnns for text summarization"
    - "a neural attention model for abstractive sentence summarization"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "query focused extractive video summarization"):
  with members:
    - "predicting important objects for egocentric video summarization"
    - "videoset video summary evaluation through text"
    - "learning determinantal point processes"
    - "markov determinantal point processes"

Connected through hyperedge HE-7 (induced by source paper "different approaches for identifying important concepts in probabilistic biomedical text summarization"):
  with members:
    - "quantifying the informativeness for biomedical literature summarization an itemset mining method"
    - "summarization from medical documents a survey"
    - "lexrank graph based lexical centrality as salience in text summarization"

Connected through hyperedge HE-8 (induced by source paper "a bayesian approach to biomedical text summarization"):
  with members:
    - "summarization from medical documents a survey"
    - "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.CL(Computation and Language)