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: "seeing what is not there learning context to determine where objects are missing"
  Abstract: Most of computer vision focuses on what is in an image. We propose to train a standalone object-centric context representation to perform the opposite task: seeing what is not there. Given an image, our context model can predict where objects should exist, even when no object instances are present. Combined with object detection results, we can perform a novel vision task: finding where objects are missing in an image. Our model is based on a convolutional neural network structure. With a specially designed training strategy, the model learns to ignore objects and focus on context only. It is fully convolutional thus highly efficient. Experiments show the effectiveness of the proposed approach in one important accessibility task: finding city street regions where curb ramps are missing, which could help millions of people with mobility disabilities.

Connected through hyperedge HE-1 (induced by source paper "generating 3d people in scenes without people"):
  with members:
    - "amass archive of motion capture as surface shapes"
    - "learning elementary structures for 3d shape generation and matching"
    - "does computer vision matter for action"
    - "resolving 3d human pose ambiguities with 3d scene constraints"
    - "the replica dataset a digital replica of indoor spaces"
    - "holistic scene understanding single view 3d holistic scene parsing and human pose estimation with human object interaction and physical commonsense"
    - "expressive body capture 3d hands face and body from a single image"
    - "imapper interaction guided scene mapping from monocular videos"

Connected through hyperedge HE-2 (induced by source paper "learning a layout transfer network for context aware object detection"):
  with members:
    - "sinet a scale insensitive convolutional neural network for fast vehicle detection"
    - "learning to look around objects for top view representations of outdoor scenes"
    - "a general pipeline for 3d detection of vehicles"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "dsod learning deeply supervised object detectors from scratch"
    - "foveanet perspective aware urban scene parsing"
    - "deep manta a coarse to fine many task network for joint 2d and 3d vehicle analysis from monocular image"
    - "accurate single stage detector using recurrent rolling convolution"

Connected through hyperedge HE-3 (induced by source paper "putting visual object recognition in context"):
  with members:
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "lift the flap context reasoning using object centered graphs"
    - "iterative visual reasoning beyond convolutions"
    - "yolov3 an incremental improvement"
    - "modeling visual context is key to augmenting object detection datasets"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "learning what and where to attend"
    - "learning scene gist with convolutional neural networks to improve object recognition"

Connected through hyperedge HE-4 (induced by source paper "unconstrained foreground object search"):
  with members:
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "compositional gan learning conditional image composition"
    - "compositional gan learning image conditional binary composition"
    - "guided image inpainting replacing an image region by pulling content from another image"
    - "generative image inpainting with contextual attention"
    - "billion scale similarity search with gpus"
    - "where and who automatic semantic aware person composition"
    - "deep image harmonization"

Connected through hyperedge HE-5 (induced by source paper "context aware synthesis and placement of object instances"):
  with members:
    - "video to video synthesis"
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "binge watching scaling affordance learning from sitcoms"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "yolov3 an incremental improvement"
    - "pedestrian synthesis gan generating pedestrian data in real scene and beyond"
    - "multimodal unsupervised image to image translation"
    - "semi parametric image synthesis"

Connected through hyperedge HE-6 (induced by source paper "where and who automatic semantic aware person composition"):
  with members:
    - "binge watching scaling affordance learning from sitcoms"
    - "photographic image synthesis with cascaded refinement networks"
    - "high resolution image inpainting using multi scale neural patch synthesis"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "context encoders feature learning by inpainting"
    - "deep residual learning for image recognition"
    - "multi scale context aggregation by dilated convolutions"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-7 (induced by source paper "object detection can be improved using human derived contextual expectations"):
  with members:
    - "semantic understanding of scenes through the ade20k dataset"
    - "object recognition with and without objects"
    - "a large scale car dataset for fine grained categorization and verification"
    - "contextual action recognition with r cnn"
    - "inside outside net detecting objects in context with skip pooling and recurrent neural networks"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"
    - "object detectors emerge in deep scene cnns"

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)