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: "leveraging visual question answering for image caption ranking"
  Abstract: Visual Question Answering (VQA) is the task of taking as input an image and a free-form natural language question about the image, and producing an accurate answer. In this work we view VQA as a "feature extraction" module to extract image and caption representations. We employ these representations for the task of image-caption ranking. Each feature dimension captures (imagines) whether a fact (question-answer pair) could plausibly be true for the image and caption. This allows the model to interpret images and captions from a wide variety of perspectives. We propose score-level and representation-level fusion models to incorporate VQA knowledge in an existing state-of-the-art VQA-agnostic image-caption ranking model. We find that incorporating and reasoning about consistency between images and captions significantly improves performance. Concretely, our model improves state-of-the-art on caption retrieval by 7.1% and on image retrieval by 4.4% on the MSCOCO dataset.

Connected through hyperedge HE-1 (induced by source paper "deep multimodal image text embeddings for automatic cross media retrieval"):
  with members:
    - "cnn cnn convolutional decoders for image captioning"
    - "stacked cross attention for image text matching"
    - "look imagine and match improving textual visual cross modal retrieval with generative models"
    - "twitter100k a real world dataset for weakly supervised cross media retrieval"
    - "dual path convolutional image text embedding with instance loss"
    - "aggregated residual transformations for deep neural networks"
    - "instance aware image and sentence matching with selective multimodal lstm"
    - "linking image and text with 2 way nets"

Connected through hyperedge HE-2 (induced by source paper "adversarial representation learning for text to image matching"):
  with members:
    - "d sne domain adaptation using stochastic neighborhood embedding"
    - "mirrorgan learning text to image generation by redescription"
    - "in defense of the triplet loss for visual recognition"
    - "boosting standard classification architectures through a ranking regularizer"
    - "man moment alignment network for natural language moment retrieval via iterative graph adjustment"
    - "partial adversarial domain adaptation"
    - "deep contextualized word representations"
    - "learnable pins cross modal embeddings for person identity"

Connected through hyperedge HE-3 (induced by source paper "polysemous visual semantic embedding for cross modal retrieval"):
  with members:
    - "on the convergence of adam and beyond"
    - "image2gif generating cinemagraphs using recurrent deep q networks"
    - "stacked cross attention for image text matching"
    - "cross modal retrieval with implicit concept association"
    - "modeling uncertainty with hedged instance embedding"
    - "look imagine and match improving textual visual cross modal retrieval with generative models"
    - "attention is all you need"
    - "learning robust visual semantic embeddings"

Connected through hyperedge HE-4 (induced by source paper "visual semantic reasoning for image text matching"):
  with members:
    - "scene graph generation with external knowledge and image reconstruction"
    - "multimodal style transfer via graph cuts"
    - "iterative visual reasoning beyond convolutions"
    - "stacked cross attention for image text matching"
    - "support neighbor loss for person re identification"
    - "visual semantic navigation using scene priors"
    - "tell me where to look guided attention inference network"
    - "exploring visual relationship for image captioning"

Connected through hyperedge HE-5 (induced by source paper "multilevel language and vision integration for text to clip retrieval"):
  with members:
    - "learning unsupervised visual grounding through semantic self supervision"
    - "joint event detection and description in continuous video streams"
    - "modulating early visual processing by language"
    - "objects that sound"
    - "learning two branch neural networks for image text matching tasks"
    - "single shot temporal action detection"
    - "dense captioning events in videos"
    - "r c3d region convolutional 3d network for temporal activity detection"

Connected through hyperedge HE-6 (induced by source paper "semantic bottleneck for computer vision tasks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "interpreting cnns via decision trees"
    - "explanation methods in deep learning users values concerns and challenges"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "methods for interpreting and understanding deep neural networks"
    - "explaining explanations an overview of interpretability of machine learning"
    - "a survey of methods for explaining black box models"

Connected through hyperedge HE-7 (induced by source paper "multi task learning of hierarchical vision language representation"):
  with members:
    - "neural baby talk"
    - "learning to count objects in natural images for visual question answering"
    - "interpretable and globally optimal prediction for textual grounding using image concepts"
    - "learning visual knowledge memory networks for visual question answering"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "visual question generation as dual task of visual question answering"
    - "vse improving visual semantic embeddings with hard negatives"

Connected through hyperedge HE-8 (induced by source paper "knowledge aided consistency for weakly supervised phrase grounding"):
  with members:
    - "motion appearance co memory networks for video question answering"
    - "query guided regression network with context policy for phrase grounding"
    - "vqs linking segmentations to questions and answers for supervised attention in vqa and question focused semantic segmentation"
    - "weakly supervised visual grounding of phrases with linguistic structures"
    - "tall temporal activity localization via language query"
    - "visual translation embedding network for visual relation detection"
    - "ambient sound provides supervision for visual learning"
    - "grounding of textual phrases in images by reconstruction"

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)