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: "seeds superpixels extracted via energy driven sampling"
  Abstract: Superpixel algorithms aim to over-segment the image by grouping pixels that belong to the same object. Many state-of-the-art superpixel algorithms rely on minimizing objective functions to enforce color ho- mogeneity. The optimization is accomplished by sophis- ticated methods that progressively build the superpix- els, typically by adding cuts or growing superpixels. As a result, they are computationally too expensive for real-time applications. We introduce a new approach based on a simple hill-climbing optimization. Starting from an initial superpixel partitioning, it continuously refines the superpixels by modifying the boundaries. We define a robust and fast to evaluate energy function, based on enforcing color similarity between the bound- aries and the superpixel color histogram. In a series of experiments, we show that we achieve an excellent com- promise between accuracy and efficiency. We are able to achieve a performance comparable to the state-of- the-art, but in real-time on a single Intel i7 CPU at 2.8GHz.

Connected through hyperedge HE-1 (induced by source paper "point cloud oversegmentation with graph structured deep metric learning"):
  with members:
    - "clustering with deep learning taxonomy and new methods"
    - "group normalization"
    - "superpixels an evaluation of the state of the art"
    - "superpixel sampling networks"
    - "know what your neighbors do 3d semantic segmentation of point clouds"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "graph based over segmentation methods for 3d point clouds"
    - "large scale point cloud semantic segmentation with superpoint graphs"

Connected through hyperedge HE-2 (induced by source paper "tapa mvs textureless aware patchmatch multi view stereo"):
  with members:
    - "mesh based camera pairs selection and occlusion aware masking for mesh refinement"
    - "a data driven prior on facet orientation for semantic mesh labeling"
    - "deepmvs learning multi view stereopsis"
    - "multi view stereo with asymmetric checkerboard propagation and multi hypothesis joint view selection"
    - "mvsnet depth inference for unstructured multi view stereo"
    - "mvdepthnet real time multiview depth estimation neural network"
    - "multi view stereo with single view semantic mesh refinement"
    - "semantically informed multiview surface refinement"

Connected through hyperedge HE-3 (induced by source paper "robust superpixels using color and contour features along linear path"):
  with members:
    - "evaluation framework of superpixel methods with a global regularity measure"
    - "superpatchmatch an algorithm for robust correspondences using superpixel patches"
    - "robust shape regularity criteria for superpixel evaluation"
    - "scalp superpixels with contour adherence using linear path"
    - "superpixels an evaluation of the state of the art"
    - "gasp geometric association with surface patches"

Connected through hyperedge HE-4 (induced by source paper "automatic segmentation of trees in dynamic outdoor environments"):
  with members:
    - "apple flower detection using deep convolutional networks"
    - "a robotic vision system to measure tree traits"

Connected through hyperedge HE-5 (induced by source paper "superpixels an evaluation of the state of the art"):
  with members:
    - "superpixel convolutional networks using bilateral inceptions"
    - "deep convolutional neural fields for depth estimation from a single image"

Connected through hyperedge HE-6 (induced by source paper "what makes for effective detection proposals"):
  with members:
    - "object proposal evaluation protocol is gameable"
    - "learning to segment object candidates"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "deepbox learning objectness with convolutional networks"
    - "learning to segment moving objects in videos"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "scalable high quality object detection"

Connected through hyperedge HE-7 (induced by source paper "spikes superpixel keypoints structure for robust visual tracking"):
  with members:
    - "high speed tracking with kernelized correlation filters"

Connected through hyperedge HE-8 (induced by source paper "beyond pixels a comprehensive survey from bottom up to semantic image segmentation and cosegmentation"):
  with members:
    - "salient object detection a survey"
    - "conditional random fields as recurrent neural networks"
    - "diagnosing state of the art object proposal methods"
    - "convolutional feature masking for joint object and stuff segmentation"
    - "simultaneous detection and segmentation"
    - "deeppose human pose estimation via deep neural networks"
    - "how good are detection proposals really"
    - "fully convolutional networks 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)