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: "learning to look around objects for top view representations of outdoor scenes"
  Abstract: Given a single RGB image of a complex outdoor road scene in the perspective view, we address the novel problem of estimating an occlusion-reasoned semantic scene layout in the top-view. This challenging problem not only requires an accurate understanding of both the 3D geometry and the semantics of the visible scene, but also of occluded areas. We propose a convolutional neural network that learns to predict occluded portions of the scene layout by looking around foreground objects like cars or pedestrians. But instead of hallucinating RGB values, we show that directly predicting the semantics and depths in the occluded areas enables a better transformation into the top-view. We further show that this initial top-view representation can be significantly enhanced by learning priors and rules about typical road layouts from simulated or, if available, map data. Crucially, training our model does not require costly or subjective human annotations for occluded areas or the top-view, but rather uses readily available annotations for standard semantic segmentation. We extensively evaluate and analyze our approach on the KITTI and Cityscapes data sets.

Connected through hyperedge HE-1 (induced by source paper "monolayout amodal scene layout from a single image"):
  with members:
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "wise ale wide sample estimator for approximate latent embedding"
    - "monocular semantic occupancy grid mapping with convolutional variational encoder decoder networks"
    - "pixor real time 3d object detection from point clouds"
    - "learning 2d to 3d lifting for object detection in 3d for autonomous vehicles"
    - "pseudo lidar accurate depth for 3d object detection in autonomous driving"
    - "gs3d an efficient 3d object detection framework for autonomous driving"
    - "probabilistic object detection definition and evaluation"

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"
    - "a general pipeline for 3d detection of vehicles"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "seeing what is not there learning context to determine where objects are missing"
    - "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 "semantic foreground inpainting from weak supervision"):
  with members:
    - "privacy protection in street view panoramas using depth and multi view imagery"
    - "monocular semantic occupancy grid mapping with convolutional variational encoder decoder networks"
    - "hallucinating beyond observation learning to complete with partial observation and unpaired prior knowledge"
    - "training of convolutional networks on multiple heterogeneous datasets for street scene semantic segmentation"
    - "free form image inpainting with gated convolution"
    - "patch based image inpainting with generative adversarial networks"
    - "generative image inpainting with contextual attention"
    - "generative face completion"

Connected through hyperedge HE-4 (induced by source paper "infer intermediate representations for future prediction"):
  with members:
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "socially aware kalman neural networks for trajectory prediction"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "sequence to sequence prediction of vehicle trajectory via lstm encoder decoder architecture"
    - "multi modal trajectory prediction of surrounding vehicles with maneuver based lstms"
    - "pyramid stereo matching network"
    - "desire distant future prediction in dynamic scenes with interacting agents"

Connected through hyperedge HE-5 (induced by source paper "hallucinating beyond observation learning to complete with partial observation and unpaired prior knowledge"):
  with members:
    - "monocular semantic occupancy grid mapping with convolutional variational encoder decoder networks"
    - "semantic road layout understanding by generative adversarial inpainting"
    - "patch based image inpainting with generative adversarial networks"
    - "generative face completion"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "semantic image inpainting with deep generative models"
    - "context encoders feature learning by inpainting"

Connected through hyperedge HE-6 (induced by source paper "the right angled perspective improving the understanding of road scenes using boosted inverse perspective mapping"):
  with members:
    - "deep object tracking on dynamic occupancy grid maps using rnns"
    - "compositional gan learning conditional image composition"
    - "towards end to end lane detection an instance segmentation approach"
    - "cross view image synthesis using geometry guided conditional gans"
    - "compositional gan learning image conditional binary composition"
    - "generative adversarial frontal view to bird view synthesis"
    - "naturalistic driver intention and path prediction using recurrent neural networks"
    - "desire distant future prediction in dynamic scenes with interacting agents"

Connected through hyperedge HE-7 (induced by source paper "a parametric top view representation of complex road scenes"):
  with members:
    - "learning to adapt structured output space for semantic segmentation"
    - "layer structured 3d scene inference via view synthesis"
    - "structured attention guided convolutional neural fields for monocular depth estimation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "im2pano3d extrapolating 360 structure and semantics beyond the field of view"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "playing for benchmarks"
    - "in place activated batchnorm for memory optimized training of dnns"

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)