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: "a noisy channel model for document compression"
  Abstract: We present a document compression system that uses a hierarchical noisy-channel model of text production. Our compression system first automatically derives the syntactic structure of each sentence and the overall discourse structure of the text given as input. The system then uses a statistical hierarchical model of text production in order to drop non-important syntactic and discourse constituents so as to generate coherent, grammatical document compressions of arbitrary length. The system outperforms both a baseline and a sentence-based compression system that operates by simplifying sequentially all sentences in a text. Our results support the claim that discourse knowledge plays an important role in document summarization.

Connected through hyperedge HE-1 (induced by source paper "joint parsing and generation for abstractive summarization"):
  with members:
    - "analyzing sentence fusion in abstractive summarization"
    - "controlling the amount of verbatim copying in abstractive summarization"
    - "scoring sentence singletons and pairs for abstractive summarization"
    - "deep communicating agents for abstractive summarization"
    - "improving abstraction in text summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "generating wikipedia by summarizing long sequences"
    - "bottom up abstractive summarization"

Connected through hyperedge HE-2 (induced by source paper "towards annotating and creating sub sentence summary highlights"):
  with members:
    - "guiding extractive summarization with question answering rewards"
    - "deep communicating agents for abstractive summarization"
    - "bottom up abstractive summarization"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adapting the neural encoder decoder framework from single to multi document summarization"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "attention is all you need"
    - "get to the point summarization with pointer generator networks"

Connected through hyperedge HE-3 (induced by source paper "tackling sequence to sequence mapping problems with neural networks"):
  with members:
    - "online and linear time attention by enforcing monotonic alignments"
    - "online segment to segment neural transduction"
    - "language as a latent variable discrete generative models for sentence compression"
    - "learning to compose words into sentences with reinforcement learning"
    - "improving neural machine translation models with monolingual data"
    - "a neural attention model for abstractive sentence summarization"
    - "effective approaches to attention based neural machine translation"
    - "abcnn attention based convolutional neural network for modeling sentence pairs"

Connected through hyperedge HE-4 (induced by source paper "structure infused copy mechanisms for abstractive summarization"):
  with members:
    - "toward abstractive summarization using semantic representations"
    - "selective encoding for abstractive sentence summarization"
    - "deep recurrent generative decoder for abstractive text summarization"
    - "efficient summarization with read again and copy mechanism"
    - "improved neural machine translation with a syntax aware encoder and decoder"
    - "get to the point summarization with pointer generator networks"
    - "modeling source syntax for neural machine translation"
    - "faithful to the original fact aware neural abstractive summarization"

Connected through hyperedge HE-5 (induced by source paper "deepchannel salience estimation by contrastive learning for extractive document summarization"):
  with members:
    - "learning to extract coherent summary via deep reinforcement learning"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "an efficient framework for learning sentence representations"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "get to the point summarization with pointer generator networks"
    - "controllable abstractive summarization"
    - "a structured self attentive sentence embedding"
    - "a deep reinforced model for abstractive summarization"

Connected through hyperedge HE-6 (induced by source paper "multiple instance learning networks for fine grained sentiment analysis"):
  with members:
    - "neural summarization by extracting sentences and words"
    - "summarunner a recurrent neural network based sequence model for extractive summarization of documents"
    - "better document level sentiment analysis from rst discourse parsing"
    - "distributed representations of sentences and documents"
    - "effective use of word order for text categorization with convolutional neural networks"
    - "neural machine translation by jointly learning to align and translate"
    - "adadelta an adaptive learning rate method"
    - "seeing stars exploiting class relationships for sentiment categorization with respect to rating scales"

Connected through hyperedge HE-7 (induced by source paper "cross lingual rst discourse parsing"):
  with members:
    - "a strong baseline for learning cross lingual word embeddings from sentence alignments"
    - "better document level sentiment analysis from rst discourse parsing"

Connected through hyperedge HE-8 (induced by source paper "a neural attention model for abstractive sentence summarization"):
  with members:
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "sequence to sequence learning with neural networks"
    - "addressing the rare word problem in neural machine translation"
    - "improving neural networks by preventing co adaptation of feature detectors"

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)