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: "detachable object detection segmentation and depth ordering from short baseline video"
  Abstract: We describe an approach for segmenting an image into regions that correspond to surfaces in the scene that are partially surrounded by the medium. It integrates both appearance and motion statistics into a cost functional, that is seeded with occluded regions and minimized efficiently by solving a linear programming problem. Where a short observation time is insufficient to determine whether the object is detachable, the results of the minimization can be used to seed a more costly optimization based on a longer sequence of video data. The result is an entirely unsupervised scheme to detect and segment an arbitrary and unknown number of objects. We test our scheme to highlight the potential, as well as limitations, of our approach.

Connected through hyperedge HE-1 (induced by source paper "occlusion shared and feature separated network for occlusion relationship reasoning"):
  with members:
    - "context constrained accurate contour extraction for occlusion edge detection"
    - "a novel multi layer framework for tiny obstacle discovery"
    - "deepflux for skeletons in the wild"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "dilated residual networks"
    - "understanding convolution for semantic segmentation"
    - "rethinking atrous convolution for semantic image segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-2 (induced by source paper "ddnet dual path decoder network for occlusion relationship reasoning"):
  with members:
    - "context constrained accurate contour extraction for occlusion edge detection"
    - "bi directional cascade network for perceptual edge detection"
    - "occlusion shared and feature separated network for occlusion relationship reasoning"
    - "deep crisp boundaries from boundaries to higher level tasks"
    - "learning to predict crisp boundaries"
    - "richer convolutional features for edge detection"
    - "object contour detection with a fully convolutional encoder decoder network"
    - "doc deep occlusion estimation from a single image"

Connected through hyperedge HE-3 (induced by source paper "visual representations defining properties and deep approximations"):
  with members:
    - "on invariance and selectivity in representation learning"
    - "sketch a net that beats humans"
    - "understanding deep image representations by inverting them"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"
    - "caffe convolutional architecture for fast feature embedding"
    - "domain size pooling in local descriptors dsp sift"
    - "multi scale orderless pooling of deep convolutional activation features"
    - "descriptor matching with convolutional neural networks a comparison to sift"

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)