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: "gslicr slic superpixels at over 250hz"
  Abstract: We introduce a parallel GPU implementation of the Simple Linear Iterative Clustering (SLIC) superpixel segmentation. Using a single graphic card, our implementation achieves speedups of up to $83\times$ from the standard sequential implementation. Our implementation is fully compatible with the standard sequential implementation and the software is now available online and is open source.

Connected through hyperedge HE-1 (induced by source paper "multimodal obstacle detection in unstructured environments with conditional random fields"):
  with members:
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "multi view 3d object detection network for autonomous driving"
    - "deep residual learning for image recognition"
    - "multimodal deep learning for robust rgb d object recognition"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "fully convolutional networks for semantic segmentation"
    - "efficient inference in fully connected crfs with gaussian edge potentials"

Connected through hyperedge HE-2 (induced by source paper "recognizing image objects by relational analysis using heterogeneous superpixels and deep convolutional features"):
  with members:
    - "efficient semantic image segmentation with superpixel pooling"
    - "deformable convnets v2 more deformable better results"
    - "dynamic routing between capsules"
    - "understanding convolution for semantic segmentation"
    - "learning depth from single monocular images using deep convolutional neural fields"
    - "u net convolutional networks for biomedical image segmentation"
    - "very deep convolutional networks for large scale image recognition"
    - "feedforward semantic segmentation with zoom out features"

Connected through hyperedge HE-3 (induced by source paper "superpixel sampling networks"):
  with members:
    - "scalp superpixels with contour adherence using linear path"
    - "clustering with deep learning taxonomy and new methods"
    - "neural expectation maximization"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "tagger deep unsupervised perceptual grouping"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "superpixel convolutional networks using bilateral inceptions"
    - "deep clustering discriminative embeddings for segmentation and separation"

Connected through hyperedge HE-4 (induced by source paper "efficient semantic image segmentation with superpixel pooling"):
  with members:
    - "superpixels an evaluation of the state of the art"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "learning rgb d salient object detection using background enclosure depth contrast and top down features"):
  with members:
    - "rgbd salient object detection via deep fusion"
    - "deep saliency with encoded low level distance map and high level features"
    - "deep contrast learning for salient object detection"
    - "salient object detection a benchmark"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "learning rich features from rgb d images for object detection and segmentation"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-6 (induced by source paper "co fusion real time segmentation tracking and fusion of multiple objects"):
  with members:
    - "virtual worlds as proxy for multi object tracking analysis"
    - "learning to refine object segments"
    - "reconstructing articulated rigged models from rgb d videos"
    - "efficient inference in fully connected crfs with gaussian edge potentials"

Connected through hyperedge HE-7 (induced by source paper "video propagation networks"):
  with members:
    - "fast optical flow using dense inverse search"
    - "learning video object segmentation from static images"
    - "clockwork convnets for video semantic segmentation"
    - "one shot video object segmentation"
    - "playing for data ground truth from computer games"
    - "learning sparse high dimensional filters image filtering dense crfs and bilateral neural networks"
    - "conditional random fields as recurrent neural networks"
    - "semantic video segmentation exploring inference efficiency"

Connected through hyperedge HE-8 (induced by source paper "superpixel segmentation using gaussian mixture model"):
  with members:
    - "learning from weak and noisy labels for semantic segmentation"

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)