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: "pixels to graphs by associative embedding"
  Abstract: Graphs are a useful abstraction of image content. Not only can graphs represent details about individual objects in a scene but they can capture the interactions between pairs of objects. We present a method for training a convolutional neural network such that it takes in an input image and produces a full graph definition. This is done end-to-end in a single stage with the use of associative embeddings. The network learns to simultaneously identify all of the elements that make up a graph and piece them together. We benchmark on the Visual Genome dataset, and demonstrate state-of-the-art performance on the challenging task of scene graph generation.

Connected through hyperedge HE-1 (induced by source paper "object centric image generation from layouts"):
  with members:
    - "interactive image generation using scene graphs"
    - "scene graph generation with external knowledge and image reconstruction"
    - "object driven text to image synthesis via adversarial training"
    - "image synthesis from reconfigurable layout and style"
    - "pastegan a semi parametric method to generate image from scene graph"
    - "locality and compositionality in zero shot learning"
    - "semantic image synthesis with spatially adaptive normalization"
    - "semantics disentangling for text to image generation"

Connected through hyperedge HE-2 (induced by source paper "bridging knowledge graphs to generate scene graphs"):
  with members:
    - "knowledge embedded routing network for scene graph generation"
    - "entity relation and event extraction with contextualized span representations"
    - "scene graph generation with external knowledge and image reconstruction"
    - "learning by abstraction the neural state machine"
    - "an empirical study on leveraging scene graphs for visual question answering"
    - "class balanced loss based on effective number of samples"
    - "question answering as global reasoning over semantic abstractions"
    - "zero shot recognition via semantic embeddings and knowledge graphs"

Connected through hyperedge HE-3 (induced by source paper "weakly supervised visual semantic parsing"):
  with members:
    - "from recognition to cognition visual commonsense reasoning"
    - "linknet relational embedding for scene graph"
    - "iterative visual reasoning beyond convolutions"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "learning human object interactions by graph parsing neural networks"
    - "structure inference net object detection using scene level context and instance level relationships"
    - "explainable and explicit visual reasoning over scene graphs"

Connected through hyperedge HE-4 (induced by source paper "classifying all interacting pairs in a single shot"):
  with members:
    - "detecting human object interactions via functional generalization"
    - "detecting unseen visual relations using analogies"
    - "ican instance centric attention network for human object interaction detection"
    - "detecting visual relationships using box attention"
    - "learning human object interactions by graph parsing neural networks"
    - "transferable interactiveness knowledge for human object interaction detection"
    - "object level visual reasoning in videos"
    - "detecting and recognizing human object interactions"

Connected through hyperedge HE-5 (induced by source paper "unbiased scene graph generation via rich and fair semantic extraction"):
  with members:
    - "knowledge embedded routing network for scene graph generation"
    - "graph r cnn for scene graph generation"
    - "attentive relational networks for mapping images to scene graphs"
    - "visual relationship detection based on guided proposals and semantic knowledge distillation"
    - "factorizable net an efficient subgraph based framework for scene graph generation"
    - "neural motifs scene graph parsing with global context"
    - "weakly supervised learning of visual relations"
    - "detecting visual relationships with deep relational networks"

Connected through hyperedge HE-6 (induced by source paper "learning a directional soft lane affordance model for road scenes using self supervision"):
  with members:
    - "self driving cars a survey"
    - "learning human aware path planning with fully convolutional networks"
    - "graph r cnn for scene graph generation"
    - "learning long range perception using self supervision from short range sensors and odometry"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "short term motion prediction of traffic actors for autonomous driving using deep convolutional networks"
    - "a parametric top view representation of complex road scenes"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-7 (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-8 (induced by source paper "zero shot generation of human object interaction videos"):
  with members:
    - "video relationship reasoning using gated spatio temporal energy graph"
    - "cgans with projection discriminator"
    - "recycle gan unsupervised video retargeting"
    - "spectral normalization for generative adversarial networks"
    - "probabilistic video generation using holistic attribute control"
    - "large scale gan training for high fidelity natural image synthesis"
    - "videos as space time region graphs"
    - "image generation from scene graphs"

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)