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: "real time semantic image segmentation via spatial sparsity"
  Abstract: We propose an approach to semantic (image) segmentation that reduces the computational costs by a factor of 25 with limited impact on the quality of results. Semantic segmentation has a number of practical applications, and for most such applications the computational costs are critical. The method follows a typical two-column network structure, where one column accepts an input image, while the other accepts a half-resolution version of that image. By identifying specific regions in the full-resolution image that can be safely ignored, as well as carefully tailoring the network structure, we can process approximately 15 highresolution Cityscapes images (1024x2048) per second using a single GTX 980 video card, while achieving a mean intersection-over-union score of 72.9% on the Cityscapes test set.

Connected through hyperedge HE-1 (induced by source paper "real time high performance semantic image segmentation of urban street scenes"):
  with members:
    - "in defense of pre trained imagenet architectures for real time semantic segmentation of road driving images"
    - "lednet a lightweight encoder decoder network for real time semantic segmentation"
    - "dfanet deep feature aggregation for real time semantic segmentation"
    - "vortex pooling improving context representation in semantic segmentation"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "yolov3 an incremental improvement"
    - "light weight refinenet for real time semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "dfanet deep feature aggregation for real time semantic segmentation"):
  with members:
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "espnet end to end speech processing toolkit"
    - "learning a discriminative feature network for semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "pyramid attention network for semantic segmentation"
    - "context encoding for semantic segmentation"
    - "deep layer aggregation"
    - "squeeze and excitation networks"

Connected through hyperedge HE-3 (induced by source paper "associative embedding for game agnostic team discrimination"):
  with members:
    - "cornernet detecting objects as paired keypoints"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "guided upsampling network for real time semantic segmentation"
    - "contextnet exploring context and detail for semantic segmentation in real time"
    - "tracking emerges by colorizing videos"
    - "generating long term trajectories using deep hierarchical networks"
    - "icnet for real time semantic segmentation on high resolution images"
    - "enet a deep neural network architecture for real time semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "real time semantic segmentation via multiply spatial fusion network"):
  with members:
    - "dfanet deep feature aggregation for real time semantic segmentation"
    - "fast scnn fast semantic segmentation network"
    - "deep layer aggregation"
    - "icnet for real time semantic segmentation on high resolution images"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "rethinking atrous convolution for semantic image segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "sgdr stochastic gradient descent with restarts"

Connected through hyperedge HE-5 (induced by source paper "bisenet bilateral segmentation network for real time semantic segmentation"):
  with members:
    - "learning a discriminative feature network for semantic segmentation"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "not all pixels are equal difficulty aware semantic segmentation via deep layer cascade"
    - "icnet for real time semantic segmentation on high resolution images"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "understanding convolution for semantic segmentation"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "residual attention network for image classification"

Connected through hyperedge HE-6 (induced by source paper "real time progressive 3d semantic segmentation for indoor scene"):
  with members:
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "3dmv joint 3d multi view prediction for 3d semantic scene segmentation"
    - "dynamic graph cnn for learning on point clouds"
    - "recurrent slice networks for 3d segmentation on point clouds"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "deep projective 3d semantic segmentation"
    - "semantic 3d occupancy mapping through efficient high order crfs"
    - "large scale point cloud semantic segmentation with superpoint graphs"

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)