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 bilingual pragmatic color references"
  Abstract: Contextual influences on language often exhibit substantial cross-lingual regularities; for example, we are more verbose in situations that require finer distinctions. However, these regularities are sometimes obscured by semantic and syntactic differences. Using a newly-collected dataset of color reference games in Mandarin Chinese (which we release to the public), we confirm that a variety of constructions display the same sensitivity to contextual difficulty in Chinese and English. We then show that a neural speaker agent trained on bilingual data with a simple multitask learning approach displays more human-like patterns of context dependence and is more pragmatically informative than its monolingual Chinese counterpart. Moreover, this is not at the expense of language-specific semantic understanding: the resulting speaker model learns the different basic color term systems of English and Chinese (with noteworthy cross-lingual influences), and it can identify synonyms between the two languages using vector analogy operations on its output layer, despite having no exposure to parallel data.

Connected through hyperedge HE-1 (induced by source paper "pragmatically informative text generation"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "a deep ensemble model with slot alignment for sequence to sequence natural language generation"
    - "pragmatically informative image captioning with character level reference"
    - "findings of the e2e nlg challenge"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "unified pragmatic models for generating and following instructions"
    - "context aware captions from context agnostic supervision"
    - "get to the point summarization with pointer generator networks"

Connected through hyperedge HE-2 (induced by source paper "communication based evaluation for natural language generation"):
  with members:
    - "unifying human and statistical evaluation for natural language generation"
    - "pragmatically informative text generation"
    - "translating neuralese"
    - "relevance of unsupervised metrics in task oriented dialogue for evaluating natural language generation"
    - "learning to summarize radiology findings"
    - "the price of debiasing automatic metrics in natural language evaluation"
    - "sentence level fluency evaluation references help but can be spared"
    - "context aware captions from context agnostic supervision"

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)