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: "relation aware graph attention network for visual question answering"
  Abstract: In order to answer semantically-complicated questions about an image, a Visual Question Answering (VQA) model needs to fully understand the visual scene in the image, especially the interactive dynamics between different objects. We propose a Relation-aware Graph Attention Network (ReGAT), which encodes each image into a graph and models multi-type inter-object relations via a graph attention mechanism, to learn question-adaptive relation representations. Two types of visual object relations are explored: (i) Explicit Relations that represent geometric positions and semantic interactions between objects; and (ii) Implicit Relations that capture the hidden dynamics between image regions. Experiments demonstrate that ReGAT outperforms prior state-of-the-art approaches on both VQA 2.0 and VQA-CP v2 datasets. We further show that ReGAT is compatible to existing VQA architectures, and can be used as a generic relation encoder to boost the model performance for VQA.

Connected through hyperedge HE-1 (induced by source paper "counterfactual samples synthesizing for robust visual question answering"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "towards causal vqa revealing and reducing spurious correlations by invariant and covariant semantic editing"
    - "don t take the premise for granted mitigating artifacts in natural language inference"
    - "don t take the easy way out ensemble based methods for avoiding known dataset biases"
    - "adversarial regularization for visual question answering strengths shortcomings and side effects"
    - "learning by abstraction the neural state machine"
    - "block bilinear superdiagonal fusion for visual question answering and visual relationship detection"
    - "simple but effective techniques to reduce biases"

Connected through hyperedge HE-2 (induced by source paper "object relational graph with teacher recommended learning for video captioning"):
  with members:
    - "spatio temporal dynamics and semantic attribute enriched visual encoding for video captioning"
    - "controllable video captioning with pos sequence guidance based on gated fusion network"
    - "memory attended recurrent network for video captioning"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "object aware aggregation with bidirectional temporal graph for video captioning"
    - "less is more picking informative frames for video captioning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "videos as space time region graphs"

Connected through hyperedge HE-3 (induced by source paper "where does it exist spatio temporal video grounding for multi form sentences"):
  with members:
    - "dynamic graph attention for referring expression comprehension"
    - "weakly supervised video moment retrieval from text queries"
    - "cross modal interaction networks for query based moment retrieval in videos"
    - "cross modal relationship inference for grounding referring expressions"
    - "man moment alignment network for natural language moment retrieval via iterative graph adjustment"
    - "object referring in videos with language and human gaze"
    - "mattnet modular attention network for referring expression comprehension"
    - "multilevel language and vision integration for text to clip retrieval"

Connected through hyperedge HE-4 (induced by source paper "cascaded human object interaction recognition"):
  with members:
    - "learning compositional neural information fusion for human parsing"
    - "reasoning visual dialogs with structural and partial observations"
    - "scene graph generation with external knowledge and image reconstruction"
    - "graphical contrastive losses for scene graph parsing"
    - "ppdm parallel point detection and matching for real time human object interaction detection"
    - "pose aware multi level feature network for human object interaction detection"
    - "understanding human gaze communication by spatio temporal graph reasoning"
    - "learning actor relation graphs for group activity recognition"

Connected through hyperedge HE-5 (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"
    - "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"
    - "detecting and recognizing human object interactions"

Connected through hyperedge HE-6 (induced by source paper "fine grained video text retrieval with hierarchical graph reasoning"):
  with members:
    - "visual semantic reasoning for image text matching"
    - "use what you have video retrieval using representations from collaborative experts"
    - "language conditioned graph networks for relational reasoning"
    - "simple bert models for relation extraction and semantic role labeling"
    - "evaluating text to image matching using binary image selection bison"
    - "neighbourhood watch referring expression comprehension via language guided graph attention networks"
    - "graph r cnn for scene graph generation"
    - "a joint sequence fusion model for video question answering and retrieval"

Connected through hyperedge HE-7 (induced by source paper "multi step reasoning via recurrent dual attention for visual dialog"):
  with members:
    - "image question answer synergistic network for visual dialog"
    - "reasoning visual dialogs with structural and partial observations"
    - "making history matter history advantage sequence training for visual dialog"
    - "dual attention networks for visual reference resolution in visual dialog"
    - "generative visual dialogue system via adaptive reasoning and weighted likelihood estimation"
    - "factor graph attention"
    - "flipdial a generative model for two way visual dialogue"
    - "ask no more deciding when to guess in referential visual dialogue"

Connected through hyperedge HE-8 (induced by source paper "an empirical study on leveraging scene graphs for visual question answering"):
  with members:
    - "rethinking visual relationships for high level image understanding"
    - "block bilinear superdiagonal fusion for visual question answering and visual relationship detection"
    - "vrr vg refocusing visually relevant relationships"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "probabilistic neural symbolic models for interpretable visual question answering"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "murel multimodal relational reasoning for visual question answering"
    - "referring relationships"

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)