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: "catching the drift probabilistic content models with applications to generation and summarization"
  Abstract: We consider the problem of modeling the content structure of texts within a specific domain, in terms of the topics the texts address and the order in which these topics appear. We first present an effective knowledge-lean method for learning content models from un-annotated documents, utilizing a novel adaptation of algorithms for Hidden Markov Models. We then apply our method to two complementary tasks: information ordering and extractive summarization. Our experiments show that incorporating content models in these applications yields substantial improvement over previously-proposed methods.

Connected through hyperedge HE-1 (induced by source paper "stochastic natural language generation using dependency information"):
  with members:
    - "end to end content and plan selection for data to text generation"
    - "a deep ensemble model with slot alignment for sequence to sequence natural language generation"
    - "findings of the e2e nlg challenge"
    - "natural language generation for spoken dialogue system using rnn encoder decoder networks"
    - "order planning neural text generation from structured data"
    - "the e2e dataset new challenges for end to end generation"
    - "table to text generation by structure aware seq2seq learning"
    - "neural text generation from structured data with application to the biography domain"

Connected through hyperedge HE-2 (induced by source paper "a survey of natural language generation techniques with a focus on dialogue systems past present and future directions"):
  with members:
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "personalized dialogue generation with diversified traits"
    - "learning to write with cooperative discriminators"
    - "training millions of personalized dialogue agents"
    - "towards empathetic open domain conversation models a new benchmark and dataset"
    - "wizard of wikipedia knowledge powered conversational agents"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "polite dialogue generation without parallel data"

Connected through hyperedge HE-3 (induced by source paper "an entity driven framework for abstractive summarization"):
  with members:
    - "discofuse a large scale dataset for discourse based sentence fusion"
    - "deep communicating agents for abstractive summarization"
    - "improving abstraction in text summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "bottom up abstractive summarization"
    - "entity commonsense representation for neural abstractive summarization"
    - "learning to extract coherent summary via deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "bigpatent a large scale dataset for abstractive and coherent summarization"):
  with members:
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "bottom up abstractive summarization"
    - "don t give me the details just the summary topic aware convolutional neural networks for extreme summarization"
    - "robust neural abstractive summarization systems and evaluation against adversarial information"
    - "a discourse aware attention model for abstractive summarization of long documents"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "detecting un important content for single document news summarization"
    - "get to the point summarization with pointer generator networks"

Connected through hyperedge HE-5 (induced by source paper "generating summaries with topic templates and structured convolutional decoders"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "bootstrapping generators from noisy data"
    - "generating wikipedia by summarizing long sequences"
    - "don t give me the details just the summary topic aware convolutional neural networks for extreme summarization"
    - "deep graph convolutional encoders for structured data to text generation"
    - "data to text generation with content selection and planning"
    - "attention is all you need"
    - "get to the point summarization with pointer generator networks"

Connected through hyperedge HE-6 (induced by source paper "deep attentive ranking networks for learning to order sentences"):
  with members:
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "context is key new approaches to neural coherence modeling"
    - "an efficient framework for learning sentence representations"
    - "attention is all you need"
    - "sentence ordering and coherence modeling using recurrent neural networks"
    - "sort story sorting jumbled images and captions into stories"
    - "neural sentence ordering"

Connected through hyperedge HE-7 (induced by source paper "graph based neural sentence ordering"):
  with members:
    - "semantic neural machine translation using amr"
    - "graph to sequence learning using gated graph neural networks"
    - "n ary relation extraction using graph state lstm"
    - "graph2seq graph to sequence learning with attention based neural networks"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "a graph to sequence model for amr to text generation"
    - "sentence state lstm for text representation"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "what comes next extractive summarization by next sentence prediction"):
  with members:
    - "get to the point summarization with pointer generator networks"
    - "a deep reinforced model for abstractive summarization"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "contextual lstm clstm models for large scale nlp tasks"
    - "neural summarization by extracting sentences and words"
    - "summarunner a recurrent neural network based sequence model for extractive summarization of documents"
    - "a neural attention model for abstractive sentence summarization"
    - "skip thought vectors"

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)