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: "vip cnn visual phrase guided convolutional neural network"
  Abstract: As the intermediate level task connecting image captioning and object detection, visual relationship detection started to catch researchers' attention because of its descriptive power and clear structure. It detects the objects and captures their pair-wise interactions with a subject-predicate-object triplet, e.g. person-ride-horse. In this paper, each visual relationship is considered as a phrase with three components. We formulate the visual relationship detection as three inter-connected recognition problems and propose a Visual Phrase guided Convolutional Neural Network (ViP-CNN) to address them simultaneously. In ViP-CNN, we present a Phrase-guided Message Passing Structure (PMPS) to establish the connection among relationship components and help the model consider the three problems jointly. Corresponding non-maximum suppression method and model training strategy are also proposed. Experimental results show that our ViP-CNN outperforms the state-of-art method both in speed and accuracy. We further pretrain ViP-CNN on our cleansed Visual Genome Relationship dataset, which is found to perform better than the pretraining on the ImageNet for this task.

Connected through hyperedge HE-1 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks"):
  with members:
    - "arbitrary shape scene text detection with adaptive text region representation"
    - "look more than once an accurate detector for text of arbitrary shapes"
    - "textfield learning a deep direction field for irregular scene text detection"
    - "omnidirectional scene text detection with sequential free box discretization"
    - "character region awareness for text detection"
    - "deep visual template free form parsing"
    - "msr multi scale shape regression for scene text detection"
    - "rethinking table recognition using graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "nodis neural ordinary differential scene understanding"):
  with members:
    - "referring relationships"
    - "graph r cnn for scene graph generation"
    - "auto encoding scene graphs for image captioning"
    - "structure inference net object detection using scene level context and instance level relationships"
    - "explainable and explicit visual reasoning over scene graphs"
    - "combining deep learning and qualitative spatial reasoning to learn complex structures from sparse examples with noise"
    - "factorizable net an efficient subgraph based framework for scene graph generation"
    - "exploring visual relationship for image captioning"

Connected through hyperedge HE-3 (induced by source paper "avr attention based salient visual relationship detection"):
  with members:
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "r vqa learning visual relation facts with semantic attention for visual question answering"
    - "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"
    - "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-4 (induced by source paper "visual semantic information pursuit a survey"):
  with members:
    - "linknet relational embedding for scene graph"
    - "iterative visual reasoning beyond convolutions"
    - "graph r cnn for scene graph generation"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "large scale visual relationship understanding"
    - "structure inference net object detection using scene level context and instance level relationships"
    - "visual relationship detection based on guided proposals and semantic knowledge distillation"
    - "factorizable net an efficient subgraph based framework for scene graph generation"

Connected through hyperedge HE-5 (induced by source paper "action genome actions as composition of spatio temporal scene graphs"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "a short note on the kinetics 700 human action dataset"
    - "graphical contrastive losses for scene graph parsing"
    - "protogan towards few shot learning for action recognition"
    - "tarn temporal attentive relation network for few shot and zero shot action recognition"
    - "visual relationships as functions enabling few shot scene graph prediction"
    - "specifying object attributes and relations in interactive scene generation"
    - "scene graph prediction with limited labels"

Connected through hyperedge HE-6 (induced by source paper "learning visual relation priors for image text matching and image captioning with neural scene graph generators"):
  with members:
    - "relational reasoning using prior knowledge for visual captioning"
    - "rethinking visual relationships for high level image understanding"
    - "object driven text to image synthesis via adversarial training"
    - "vrr vg refocusing visually relevant relationships"
    - "scene graph reasoning with prior visual relationship for visual question answering"
    - "stacked cross attention for image text matching"
    - "multi modal learning with prior visual relation reasoning"
    - "exploring visual relationship for image captioning"

Connected through hyperedge HE-7 (induced by source paper "visual relationships as functions enabling few shot scene graph prediction"):
  with members:
    - "scene graph generation with external knowledge and image reconstruction"
    - "scene graph prediction with limited labels"
    - "referring relationships"
    - "graph r cnn for scene graph generation"
    - "image generation from scene graphs"
    - "inferring and executing programs for visual reasoning"
    - "neural motifs scene graph parsing with global context"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-8 (induced by source paper "learning predicates as functions to enable few shot scene graph prediction"):
  with members:
    - "scene graph generation with external knowledge and image reconstruction"
    - "scene graph prediction with limited labels"
    - "referring relationships"
    - "graph r cnn for scene graph generation"
    - "image generation from scene graphs"
    - "factorizable net an efficient subgraph based framework for scene graph generation"
    - "inferring and executing programs for visual reasoning"
    - "neural motifs scene graph parsing with global context"

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)