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: "semantic amodal segmentation"
  Abstract: Common visual recognition tasks such as classification, object detection, and semantic segmentation are rapidly reaching maturity, and given the recent rate of progress, it is not unreasonable to conjecture that techniques for many of these problems will approach human levels of performance in the next few years. In this paper we look to the future: what is the next frontier in visual recognition? #R##N#We offer one possible answer to this question. We propose a detailed image annotation that captures information beyond the visible pixels and requires complex reasoning about full scene structure. Specifically, we create an amodal segmentation of each image: the full extent of each region is marked, not just the visible pixels. Annotators outline and name all salient regions in the image and specify a partial depth order. The result is a rich scene structure, including visible and occluded portions of each region, figure-ground edge information, semantic labels, and object overlap. #R##N#We create two datasets for semantic amodal segmentation. First, we label 500 images in the BSDS dataset with multiple annotators per image, allowing us to study the statistics of human annotations. We show that the proposed full scene annotation is surprisingly consistent between annotators, including for regions and edges. Second, we annotate 5000 images from COCO. This larger dataset allows us to explore a number of algorithmic ideas for amodal segmentation and depth ordering. We introduce novel metrics for these tasks, and along with our strong baselines, define concrete new challenges for the community.

Connected through hyperedge HE-1 (induced by source paper "layered embeddings for amodal instance segmentation"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "segan segmenting and generating the invisible"
    - "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"

Connected through hyperedge HE-2 (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-3 (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-4 (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-5 (induced by source paper "learning instance occlusion for panoptic segmentation"):
  with members:
    - "panoptic segmentation"
    - "an end to end network for panoptic segmentation"
    - "upsnet a unified panoptic segmentation network"
    - "adaptis adaptive instance selection network"
    - "mask scoring r cnn"
    - "panoptic feature pyramid networks"
    - "attention guided unified network for panoptic segmentation"
    - "learning to fuse things and stuff"

Connected through hyperedge HE-6 (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"
    - "segan segmenting and generating the invisible"

Connected through hyperedge HE-7 (induced by source paper "deep multivariate mixture of gaussians for object detection under occlusion"):
  with members:
    - "feature selective anchor free module for single shot object detection"
    - "prediction tracking segmentation"
    - "depth wise decomposition for accelerating separable convolutions in efficient convolutional neural networks"
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "amc automl for model compression and acceleration on mobile devices"
    - "bounding box regression with uncertainty for accurate object detection"
    - "deformable convnets v2 more deformable better results"
    - "sniper efficient multi scale training"

Connected through hyperedge HE-8 (induced by source paper "learning semantics aware distance map with semantics layering network for amodal instance segmentation"):
  with members:
    - "mvtec d2s densely segmented supermarket dataset"
    - "segan segmenting and generating the invisible"
    - "depth image inpainting improving low rank matrix completion with low gradient regularization"
    - "mask r cnn"
    - "pixel level encoding and depth layering for instance level semantic labeling"
    - "ssd single shot multibox detector"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "learning to refine object segments"

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)