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: "dual attention networks for multimodal reasoning and matching"
  Abstract: We propose Dual Attention Networks (DANs) which jointly leverage visual and textual attention mechanisms to capture fine-grained interplay between vision and language. DANs attend to specific regions in images and words in text through multiple steps and gather essential information from both modalities. Based on this framework, we introduce two types of DANs for multimodal reasoning and matching, respectively. The reasoning model allows visual and textual attentions to steer each other during collaborative inference, which is useful for tasks such as Visual Question Answering (VQA). In addition, the matching model exploits the two attention mechanisms to estimate the similarity between images and sentences by focusing on their shared semantics. Our extensive experiments validate the effectiveness of DANs in combining vision and language, achieving the state-of-the-art performance on public benchmarks for VQA and image-text matching.

Connected through hyperedge HE-1 (induced by source paper "vsgnet spatial attention network for detecting human object interactions using graph convolutions"):
  with members:
    - "learning actor relation graphs for group activity recognition"
    - "relation aware graph attention network for visual question answering"
    - "actor conditioned attention maps for video action detection"
    - "ican instance centric attention network for human object interaction detection"
    - "unified perceptual parsing for scene understanding"
    - "detecting visual relationships using box attention"
    - "learning human object interactions by graph parsing neural networks"
    - "transferable interactiveness knowledge for human object interaction detection"

Connected through hyperedge HE-2 (induced by source paper "adaptive offline quintuplet loss for image text matching"):
  with members:
    - "expressing objects just like words recurrent visual embedding for image text matching"
    - "visual semantic reasoning for image text matching"
    - "saliency guided attention network for image sentence matching"
    - "focus your attention a bidirectional focal attention network for image text matching"
    - "hal improved text image matching by mitigating visual semantic hubs"
    - "factual or emotional stylized image captioning with adaptive learning and attention"
    - "identity aware textual visual matching with latent co attention"
    - "dual path convolutional image text embedding with instance loss"

Connected through hyperedge HE-3 (induced by source paper "mhsan multi head self attention network for visual semantic embedding"):
  with members:
    - "univse robust visual semantic embeddings via structured semantic representations"
    - "finding beans in burgers deep semantic visual embedding with localization"
    - "bam bottleneck attention module"
    - "stacked cross attention for image text matching"
    - "cbam convolutional block attention module"
    - "simple recurrent units for highly parallelizable recurrence"
    - "look imagine and match improving textual visual cross modal retrieval with generative models"
    - "learning two branch neural networks for image text matching tasks"

Connected through hyperedge HE-4 (induced by source paper "expressing objects just like words recurrent visual embedding for image text matching"):
  with members:
    - "position focused attention network for image text matching"
    - "stacked cross attention for image text matching"
    - "look imagine and match improving textual visual cross modal retrieval with generative models"
    - "dual path convolutional image text embedding with instance loss"
    - "vse improving visual semantic embeddings with hard negatives"
    - "learning semantic concepts and order for image and sentence matching"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "areas of attention for image captioning"

Connected through hyperedge HE-5 (induced by source paper "bilinear graph networks for visual question answering"):
  with members:
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "deep modular co attention networks for visual question answering"
    - "murel multimodal relational reasoning for visual question answering"
    - "bilinear attention networks"
    - "learning to count objects in natural images for visual question answering"
    - "graph r cnn for scene graph generation"
    - "rethinking diversified and discriminative proposal generation for visual grounding"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-6 (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-7 (induced by source paper "beyond clicks modeling multi relational item graph for session based target behavior prediction"):
  with members:
    - "session based recommendation with graph neural network"
    - "bert4rec sequential recommendation with bidirectional encoder representations from transformer"
    - "hierarchical temporal convolutional networks for dynamic recommender systems"
    - "repeatnet a repeat aware neural recommendation machine for session based recommendation"
    - "attention is all you need"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "personalizing session based recommendations with hierarchical recurrent neural networks"
    - "improved recurrent neural networks for session based recommendations"

Connected through hyperedge HE-8 (induced by source paper "modality balanced models for visual dialogue"):
  with members:
    - "image question answer synergistic network for visual dialog"
    - "dual attention networks for visual reference resolution in visual dialog"
    - "recursive visual attention in visual dialog"
    - "mutan multimodal tucker fusion for visual question answering"
    - "are you talking to me reasoned visual dialog generation through adversarial learning"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "multi modal factorized bilinear pooling with co attention learning for visual question answering"
    - "hierarchical question image co attention for visual question answering"

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)