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: "amodal instance segmentation"
  Abstract: We consider the problem of amodal instance segmentation, the objective of which is to predict the region encompassing both visible and occluded parts of each object. Thus far, the lack of publicly available amodal segmentation annotations has stymied the development of amodal segmentation methods. In this paper, we sidestep this issue by relying solely on standard modal instance segmentation annotations to train our model. The result is a new method for amodal instance segmentation, which represents the first such method to the best of our knowledge. We demonstrate the proposed method's effectiveness both qualitatively and quantitatively.

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"
    - "fully convolutional instance aware semantic segmentation"
    - "semantic amodal 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 "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-6 (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-7 (induced by source paper "oriented boxes for accurate instance segmentation"):
  with members:
    - "retinamask learning to predict masks improves state of the art single shot detection for free"
    - "mask scoring r cnn"
    - "shapemask learning to segment novel objects by refining shape priors"
    - "acquire augment segment enjoy weakly supervised instance segmentation of supermarket products"
    - "arbitrary oriented scene text detection via rotation proposals"
    - "mvtec d2s densely segmented supermarket dataset"
    - "automatic ship detection in remote sensing images from google earth of complex scenes based on multiscale rotation dense feature pyramid networks"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"

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)