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: "colors in context a pragmatic neural model for grounded language understanding"
  Abstract: We present a model of pragmatic referring expression interpretation in a grounded communication task (identifying colors from descriptions) that draws upon predictions from two recurrent neural network classifiers, a speaker and a listener, unified by a recursive pragmatic reasoning framework. Experiments show that this combined pragmatic model interprets color descriptions more accurately than the classifiers from which it is built, and that much of this improvement results from combining the speaker and listener perspectives. We observe that pragmatic reasoning helps primarily in the hardest cases: when the model must distinguish very similar colors, or when few utterances adequately express the target color. Our findings make use of a newly-collected corpus of human utterances in color reference games, which exhibit a variety of pragmatic behaviors. We also show that the embedded speaker model reproduces many of these pragmatic behaviors.

Connected through hyperedge HE-1 (induced by source paper "towards learning a generic agent for vision and language navigation via pre training"):
  with members:
    - "vision and dialog navigation"
    - "tactical rewind self correction via backtracking in vision and language navigation"
    - "lxmert learning cross modality encoder representations from transformers"
    - "unicoder vl a universal encoder for vision and language by cross modal pre training"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "robust navigation with language pretraining and stochastic sampling"
    - "videobert a joint model for video and language representation learning"
    - "vl bert pre training of generic visual linguistic representations"

Connected through hyperedge HE-2 (induced by source paper "multi view learning for vision and language navigation"):
  with members:
    - "towards learning a generic agent for vision and language navigation via pre training"
    - "tactical rewind self correction via backtracking in vision and language navigation"
    - "vision language navigation with self supervised auxiliary reasoning tasks"
    - "self monitoring navigation agent via auxiliary progress estimation"
    - "the regretful agent heuristic aided navigation through progress estimation"
    - "learning to navigate unseen environments back translation with environmental dropout"
    - "reinforced cross modal matching and self supervised imitation learning for vision language navigation"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"

Connected through hyperedge HE-3 (induced by source paper "learning of colors from color names distribution and point estimation"):
  with members:
    - "neural network with unbounded activation functions is universal approximator"
    - "learning to generalize to new compositions in image understanding"
    - "learning to generate compositional color descriptions"
    - "enriching word vectors with subword information"
    - "generative adversarial text to image synthesis"
    - "character sequence models for colorfulwords"
    - "pixel recurrent neural networks"
    - "generating images from captions with attention"

Connected through hyperedge HE-4 (induced by source paper "multimodal generative models for compositional representation learning"):
  with members:
    - "factorized inference in deep markov models for incomplete multimodal time series"
    - "measuring compositionality in representation learning"
    - "vae gans for probabilistic compressive image recovery uncertainty analysis"
    - "shapeglot learning language for shape differentiation"
    - "neural autoregressive flows"
    - "graph r cnn for scene graph generation"
    - "yolov3 an incremental improvement"
    - "multimodal matching using a hybrid convolutional neural network"

Connected through hyperedge HE-5 (induced by source paper "pragmatically informative text generation"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "generating bilingual pragmatic color references"
    - "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"

Connected through hyperedge HE-6 (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"
    - "generating bilingual pragmatic color references"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "is the red square big malevic modeling adjectives leveraging visual contexts"):
  with members:
    - "linguistic generalization and compositionality in modern artificial neural networks"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "how clever is the film model and how clever can it be"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "comparatives quantifiers proportions a multi task model for the learning of quantities from vision"
    - "systematic generalization what is required and can it be learned"
    - "a corpus for reasoning about natural language grounded in photographs"
    - "pragmatically informative image captioning with character level reference"

Connected through hyperedge HE-8 (induced by source paper "harnessing the richness of the linguistic signal in predicting pragmatic inferences"):
  with members:
    - "attention is not not explanation"
    - "what syntactic structures block dependencies in rnn language models"
    - "attention is not explanation"
    - "huggingface s transformers state of the art natural language processing"
    - "attention interpretability across nlp tasks"
    - "neural language models as psycholinguistic subjects representations of syntactic state"
    - "linguistic knowledge and transferability of contextual representations"
    - "allennlp a deep semantic natural language processing platform"

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)