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: "r vqa learning visual relation facts with semantic attention for visual question answering"
  Abstract: Recently, Visual Question Answering (VQA) has emerged as one of the most significant tasks in multimodal learning as it requires understanding both visual and textual modalities. Existing methods mainly rely on extracting image and question features to learn their joint feature embedding via multimodal fusion or attention mechanism. Some recent studies utilize external VQA-independent models to detect candidate entities or attributes in images, which serve as semantic knowledge complementary to the VQA task. However, these candidate entities or attributes might be unrelated to the VQA task and have limited semantic capacities. To better utilize semantic knowledge in images, we propose a novel framework to learn visual relation facts for VQA. Specifically, we build up a Relation-VQA (R-VQA) dataset based on the Visual Genome dataset via a semantic similarity module, in which each data consists of an image, a corresponding question, a correct answer and a supporting relation fact. A well-defined relation detector is then adopted to predict visual question-related relation facts. We further propose a multi-step attention model composed of visual attention and semantic attention sequentially to extract related visual knowledge and semantic knowledge. We conduct comprehensive experiments on the two benchmark datasets, demonstrating that our model achieves state-of-the-art performance and verifying the benefit of considering visual relation facts.

Connected through hyperedge HE-1 (induced by source paper "avr attention based salient visual relationship detection"):
  with members:
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "deep group shuffling random walk for person re identification"
    - "factorizable net an efficient subgraph based framework for scene graph generation"
    - "exploring visual relationship for image captioning"
    - "vip cnn visual phrase guided convolutional neural network"
    - "detecting visual relationships with deep relational networks"
    - "scene graph generation by iterative message passing"
    - "visual relationship detection with internal and external linguistic knowledge distillation"

Connected through hyperedge HE-2 (induced by source paper "relation aware graph attention network for visual question answering"):
  with members:
    - "murel multimodal relational reasoning for visual question answering"
    - "bilinear attention networks"
    - "tell and answer towards explainable visual question answering using attributes and captions"
    - "learning visual question answering by bootstrapping hard attention"
    - "learning to count objects in natural images for visual question answering"
    - "neighbourhood watch referring expression comprehension via language guided graph attention networks"
    - "differential attention for visual question answering"
    - "learning visual knowledge memory networks for visual question answering"

Connected through hyperedge HE-3 (induced by source paper "on class imbalance and background filtering in visual relationship detection"):
  with members:
    - "graph r cnn for scene graph generation"
    - "deep imbalanced attribute classification using visual attention aggregation"
    - "visual relationship prediction via label clustering and incorporation of depth information"
    - "image generation from scene graphs"
    - "deep imbalanced learning for face recognition and attribute prediction"
    - "vip cnn visual phrase guided convolutional neural network"
    - "on calibration of modern neural networks"
    - "neural motifs scene graph parsing with global context"

Connected through hyperedge HE-4 (induced by source paper "generating question relevant captions to aid visual question answering"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "self critical reasoning for robust visual question answering"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "tell and answer towards explainable visual question answering using attributes and captions"
    - "vqa e explaining elaborating and enhancing your answers for visual questions"
    - "discriminability objective for training descriptive captions"
    - "show tell and discriminate image captioning by self retrieval with partially labeled data"
    - "dynamic filtering with large sampling field for convnets"

Connected through hyperedge HE-5 (induced by source paper "vrr vg refocusing visually relevant relationships"):
  with members:
    - "graph r cnn for scene graph generation"
    - "large scale visual relationship understanding"
    - "learning conditioned graph structures for interpretable visual question answering"
    - "exploring visual relationship for image captioning"
    - "beyond bilinear generalized multi modal factorized high order pooling for visual question answering"
    - "mutan multimodal tucker fusion for visual question answering"
    - "neural motifs scene graph parsing with global context"
    - "weakly supervised learning of visual relations"

Connected through hyperedge HE-6 (induced by source paper "knowit vqa answering knowledge based questions about videos"):
  with members:
    - "passage re ranking with bert"
    - "ok vqa a visual question answering benchmark requiring external knowledge"
    - "from recognition to cognition visual commonsense reasoning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "tvqa localized compositional video question answering"
    - "from benedict cumberbatch to sherlock holmes character identification in tv series without a script"
    - "mutan multimodal tucker fusion for visual question answering"
    - "whodunnit crime drama as a case for natural language understanding"

Connected through hyperedge HE-7 (induced by source paper "a review on intelligent object perception methods combining knowledge based reasoning and machine learning"):
  with members:
    - "what object should i use task driven object detection"
    - "scene graph generation with external knowledge and image reconstruction"
    - "robocse robot common sense embedding"
    - "recent advances in imitation learning from observation"
    - "integrating knowledge and reasoning in image understanding"
    - "model free model based and general intelligence"
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "zero shot recognition via semantic embeddings and knowledge graphs"

Connected through hyperedge HE-8 (induced by source paper "question guided hybrid convolution for visual question answering"):
  with members:
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "modulating early visual processing by language"
    - "inferring and executing programs for visual reasoning"
    - "co attending free form regions and detections with multi modal multiplicative feature embedding for visual question answering"
    - "mutan multimodal tucker fusion for visual question answering"
    - "learning to reason end to end module networks for visual question answering"
    - "film visual reasoning with a general conditioning layer"
    - "scene graph generation from objects phrases and region captions"

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)