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: "multimodal convolutional neural networks for matching image and sentence"
  Abstract: In this paper, we propose multimodal convolutional neural networks (m-CNNs) for matching image and sentence. Our m-CNN provides an end-to-end framework with convolutional architectures to exploit image representation, word composition, and the matching relations between the two modalities. More specifically, it consists of one image CNN encoding the image content, and one matching CNN learning the joint representation of image and sentence. The matching CNN composes words to different semantic fragments and learns the inter-modal relations between image and the composed fragments at different levels, thus fully exploit the matching relations between image and sentence. Experimental results on benchmark databases of bidirectional image and sentence retrieval demonstrate that the proposed m-CNNs can effectively capture the information necessary for image and sentence matching. Specifically, our proposed m-CNNs for bidirectional image and sentence retrieval on Flickr30K and Microsoft COCO databases achieve the state-of-the-art performances.

Connected through hyperedge HE-1 (induced by source paper "imram iterative matching with recurrent attention memory for cross modal image text retrieval"):
  with members:
    - "visual semantic reasoning for image text matching"
    - "unicoder vl a universal encoder for vision and language by cross modal pre training"
    - "saliency guided attention network for image sentence matching"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "stacked cross attention for image text matching"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "webly supervised joint embedding for cross modal image text retrieval"

Connected through hyperedge HE-2 (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"
    - "leveraging visual question answering for image caption ranking"
    - "aggregated residual transformations for deep neural networks"
    - "instance aware image and sentence matching with selective multimodal lstm"

Connected through hyperedge HE-3 (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-4 (induced by source paper "reconstruct and represent video contents for captioning via reinforcement learning"):
  with members:
    - "learning to guide decoding for image captioning"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "regularizing rnns for caption generation by reconstructing the past with the present"
    - "end to end dense video captioning with masked transformer"
    - "jointly localizing and describing events for dense video captioning"
    - "reconstruction network for video captioning"
    - "recurrent fusion network for image captioning"
    - "reinforced video captioning with entailment rewards"

Connected through hyperedge HE-5 (induced by source paper "multi modal attention network learning for semantic source code retrieval"):
  with members:
    - "cross modal retrieval in the cooking context learning semantic text image embeddings"
    - "end to end audio visual scene aware dialog using multimodal attention based video features"
    - "multimodal dual attention memory for video story question answering"
    - "improving automatic source code summarization via deep reinforcement learning"
    - "multimodal machine learning a survey and taxonomy"
    - "attention is all you need"
    - "a deep reinforced model for abstractive summarization"
    - "dual attention networks for multimodal reasoning and matching"

Connected through hyperedge HE-6 (induced by source paper "controllable video captioning with pos sequence guidance based on gated fusion network"):
  with members:
    - "reconstruct and represent video contents for captioning via reinforcement learning"
    - "hierarchical photo scene encoder for album storytelling"
    - "less is more picking informative frames for video captioning"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "regularizing rnns for caption generation by reconstructing the past with the present"
    - "unsupervised image captioning"
    - "reconstruction network for video captioning"
    - "recurrent fusion network for image captioning"

Connected through hyperedge HE-7 (induced by source paper "target oriented deformation of visual semantic embedding space"):
  with members:
    - "camp cross modal adaptive message passing for text image retrieval"
    - "visual semantic reasoning for image text matching"
    - "polysemous visual semantic embedding for cross modal retrieval"
    - "matching images and text with multi modal tensor fusion and re ranking"
    - "learning similarity conditions without explicit supervision"
    - "sodeep a sorting deep net to learn ranking loss surrogates"
    - "focus your attention a bidirectional focal attention network for image text matching"
    - "learning visual relation priors for image text matching and image captioning with neural scene graph generators"

Connected through hyperedge HE-8 (induced by source paper "align2ground weakly supervised phrase grounding guided by image caption alignment"):
  with members:
    - "finding beans in burgers deep semantic visual embedding with localization"
    - "multi level multimodal common semantic space for image phrase grounding"
    - "using syntax to ground referring expressions in natural images"
    - "stacked cross attention for image text matching"
    - "deep sets"
    - "knowledge aided consistency for weakly supervised phrase grounding"
    - "understanding visual ads by aligning symbols and objects using co attention"
    - "learning two branch neural networks for image text matching tasks"

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)