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: "segan segmenting and generating the invisible"
  Abstract: Objects often occlude each other in scenes; Inferring their appearance beyond their visible parts plays an important role in scene understanding, depth estimation, object interaction and manipulation. In this paper, we study the challenging problem of completing the appearance of occluded objects. Doing so requires knowing which pixels to paint (segmenting the invisible parts of objects) and what color to paint them (generating the invisible parts). Our proposed novel solution, SeGAN, jointly optimizes for both segmentation and generation of the invisible parts of objects. Our experimental results show that: (a) SeGAN can learn to generate the appearance of the occluded parts of objects; (b) SeGAN outperforms state-of-the-art segmentation baselines for the invisible parts of objects; (c) trained on synthetic photo realistic images, SeGAN can reliably segment natural images; (d) by reasoning about occluder occludee relations, our method can infer depth layering.

Connected through hyperedge HE-1 (induced by source paper "the impact of hole geometry on relative robustness of in painting networks an empirical study"):
  with members:
    - "foreground aware image inpainting"
    - "image transformer"
    - "adversarial generative nets neural network attacks on state of the art face recognition"
    - "free form image inpainting with gated convolution"
    - "partial convolution based padding"
    - "generative image inpainting with contextual attention"
    - "formal guarantees on the robustness of a classifier against adversarial manipulation"
    - "high resolution image synthesis and semantic manipulation with conditional gans"

Connected through hyperedge HE-2 (induced by source paper "layered embeddings for amodal instance segmentation"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "semantic instance segmentation via deep metric learning"
    - "semantic instance segmentation with a discriminative loss function"
    - "mask r cnn"
    - "pixel level encoding and depth layering for instance level semantic labeling"
    - "amodal instance segmentation"
    - "fully convolutional instance aware semantic segmentation"
    - "semantic amodal segmentation"

Connected through hyperedge HE-3 (induced by source paper "yes we gan applying adversarial techniques for autonomous driving"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "video to video synthesis"
    - "fast converging conditional generative adversarial networks for image synthesis"
    - "multimodal unsupervised image to image translation"
    - "universal adversarial training"
    - "large scale gan training for high fidelity natural image synthesis"
    - "learning to drive from simulation without real world labels"
    - "point cloud gan"

Connected through hyperedge HE-4 (induced by source paper "embodied visual recognition"):
  with members:
    - "learning instance segmentation by interaction"
    - "yolov3 an incremental improvement"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "gibson env real world perception for embodied agents"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "active object perceiver recognition guided policy learning for object searching on mobile robots"
    - "neural modular control for embodied question answering"

Connected through hyperedge HE-5 (induced by source paper "how to make a pizza learning a compositional layer based gan model"):
  with members:
    - "recycle gan unsupervised video retargeting"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "cross modal retrieval in the cooking context learning semantic text image embeddings"
    - "composable unpaired image to image translation"
    - "multimodal unsupervised image to image translation"
    - "modular generative adversarial networks"
    - "stochastic video generation with a learned prior"
    - "ganimation anatomically aware facial animation from a single image"

Connected through hyperedge HE-6 (induced by source paper "3d point cloud generative adversarial network based on tree structured graph convolutions"):
  with members:
    - "translating and segmenting multimodal medical volumes with cycle and shape consistency generative adversarial network"
    - "multiresolution tree networks for 3d point cloud processing"
    - "tree cnn a deep convolutional neural network for lifelong learning"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "conditional image to image translation"
    - "dynamic graph cnn for learning on point clouds"
    - "vital visual tracking via adversarial learning"
    - "generative image inpainting with contextual attention"

Connected through hyperedge HE-7 (induced by source paper "3d scene graph a structure for unified semantics 3d space and camera"):
  with members:
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "configurable 3d scene synthesis and 2d image rendering with per pixel ground truth using stochastic grammars"
    - "gibson env real world perception for embodied agents"
    - "a deep learning based behavioral approach to indoor autonomous navigation"
    - "learning human object interactions by graph parsing neural networks"
    - "fluid annotation a human machine collaboration interface for full image annotation"
    - "holistic 3d scene parsing and reconstruction from a single rgb image"
    - "blitznet a real time deep network for scene understanding"

Connected through hyperedge HE-8 (induced by source paper "visualizing the invisible occluded vehicle segmentation and recovery"):
  with members:
    - "path aggregation network for instance segmentation"
    - "toward driving scene understanding a dataset for learning driver behavior and causal reasoning"
    - "event based vision meets deep learning on steering prediction for self driving cars"
    - "learning to see the invisible end to end trainable amodal instance segmentation"
    - "generative image inpainting with contextual attention"
    - "interpretable learning for self driving cars by visualizing causal attention"
    - "learning deep neural networks for vehicle re id with visual spatio temporal path proposals"
    - "fcn rlstm deep spatio temporal neural networks for vehicle counting in city cameras"

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)