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: "context aware captions from context agnostic supervision"
  Abstract: We introduce an inference technique to produce discriminative context-aware image captions (captions that describe differences between images or visual concepts) using only generic context-agnostic training data (captions that describe a concept or an image in isolation). For example, given images and captions of "siamese cat" and "tiger cat", we generate language that describes the "siamese cat" in a way that distinguishes it from "tiger cat". Our key novelty is that we show how to do joint inference over a language model that is context-agnostic and a listener which distinguishes closely-related concepts. We first apply our technique to a justification task, namely to describe why an image contains a particular fine-grained category as opposed to another closely-related category of the CUB-200-2011 dataset. We then study discriminative image captioning to generate language that uniquely refers to one of two semantically-similar images in the COCO dataset. Evaluations with discriminative ground truth for justification and human studies for discriminative image captioning reveal that our approach outperforms baseline generative and speaker-listener approaches for discrimination.

Connected through hyperedge HE-1 (induced by source paper "adversarially guided self play for adopting social conventions"):
  with members:
    - "the hanabi challenge a new frontier for ai research"
    - "learning existing social conventions via observationally augmented self play"
    - "a style based generator architecture for generative adversarial networks"
    - "emergence of grounded compositional language in multi agent populations"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "image to image translation with conditional adversarial networks"
    - "learning to communicate with deep multi agent reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "robust change captioning"):
  with members:
    - "clevr dialog a diagnostic dataset for multi round reasoning in visual dialog"
    - "clevr ref diagnosing visual reasoning with referring expressions"
    - "grounding visual explanations"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "neural baby talk"
    - "discriminability objective for training descriptive captions"
    - "learning to describe differences between pairs of similar images"

Connected through hyperedge HE-3 (induced by source paper "evaluating text to image matching using binary image selection bison"):
  with members:
    - "discriminability objective for training descriptive captions"
    - "stacked cross attention for image text matching"
    - "a corpus for reasoning about natural language grounded in photographs"
    - "exploring the limits of weakly supervised pretraining"
    - "engaging image captioning via personality"
    - "learning to ask neural question generation for reading comprehension"
    - "convolutional image captioning"
    - "vse improving visual semantic embeddings with hard negatives"

Connected through hyperedge HE-4 (induced by source paper "expressing visual relationships via language"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "learning to globally edit images with textual description"
    - "discriminability objective for training descriptive captions"
    - "learning to describe differences between pairs of similar images"
    - "a corpus for reasoning about natural language grounded in photographs"
    - "learning to color from language"
    - "pragmatically informative image captioning with character level reference"
    - "reinforced video captioning with entailment rewards"

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"
    - "get to the point summarization with pointer generator networks"

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 "neural naturalist generating fine grained image comparisons"):
  with members:
    - "robust change captioning"
    - "expressing visual relationships via language"
    - "learning to describe differences between pairs of similar images"
    - "a corpus for reasoning about natural language grounded in photographs"
    - "attention is all you need"
    - "the inaturalist species classification and detection dataset"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "generating visual explanations"

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.CV(Computer Vision and Pattern Recognition)