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: "layered interpretation of street view images"
  Abstract: We propose a layered street view model to encode both depth and semantic information on street view images for autonomous driving. Recently, stixels, stix-mantics, and tiered scene labeling methods have been proposed to model street view images. We propose a 4-layer street view model, a compact representation over the recently proposed stix-mantics model. Our layers encode semantic classes like ground, pedestrians, vehicles, buildings, and sky in addition to the depths. The only input to our algorithm is a pair of stereo images. We use a deep neural network to extract the appearance features for semantic classes. We use a simple and an efficient inference algorithm to jointly estimate both semantic classes and layered depth values. Our method outperforms other competing approaches in Daimler urban scene segmentation dataset. Our algorithm is massively parallelizable, allowing a GPU implementation with a processing speed about 9 fps.

Connected through hyperedge HE-1 (induced by source paper "improved semantic stixels via multimodal sensor fusion"):
  with members:
    - "slanted stixels representing san francisco s steepest streets"
    - "semantic3d net a new large scale point cloud classification benchmark"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "learning from synthetic data addressing domain shift for semantic segmentation"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "the stixel world"
    - "squeezeseg convolutional neural nets with recurrent crf for real time road object segmentation from 3d lidar point cloud"

Connected through hyperedge HE-2 (induced by source paper "casenet deep category aware semantic edge detection"):
  with members:
    - "boundary cues for 3d object shape recovery"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "convolutional oriented boundaries"
    - "object contour detection with a fully convolutional encoder decoder network"
    - "holistically nested edge detection"
    - "weakly supervised object boundaries"
    - "pixel wise deep learning for contour detection"
    - "high for low and low for high efficient boundary detection from deep object features and its applications to high level vision"

Connected through hyperedge HE-3 (induced by source paper "the stixel world"):
  with members:
    - "the cityscapes dataset for semantic urban scene understanding"
    - "semantic image segmentation via deep parsing network"
    - "deep hierarchical parsing for semantic segmentation"
    - "bayesian segnet model uncertainty in deep convolutional encoder decoder architectures for scene understanding"
    - "conditional random fields as recurrent neural networks"
    - "efficient piecewise training of deep structured models for semantic segmentation"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "feedforward semantic segmentation with zoom out features"

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)