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: "doc deep occlusion recovering from a single image"
  Abstract: Recovering the occlusion relationship between stuffs and things is a fundamental ability of human vision, and yields information about the 3D world from 2D images. In this paper, we propose deep occlusion (DOC), which is an end-to-end deep learning strategy that recovers the occlusion boundaries from a single image. Firstly, for each pixel along the boundaries, we introduce an orientation variable, indicating the occlusion relationship between two adjacent regions. Then, deep features, long range context and object-level knowledge are exploited by an end-to-end deep convolutional neural network (DCNN) to accurately locate the boundaries and recover the occlusion relationships. To evaluate and train the DOC network, we construct a large-scale instance occlusion boundary dataset from the PASCAL images, which we call the PASCAL instance occlusion dataset. It includes 20k images (100$\times$ bigger than the existing occlusion datasets for outdoor images) and so is big enough to train and evaluate DCNNs for occlusion learning. Finally, extensive experiments are performed to demonstrate that our approach has strong generalization ability and outperform state-of-the-art strategies.

Connected through hyperedge HE-1 (induced by source paper "human like clustering with deep convolutional neural networks"):
  with members:
    - "u net convolutional networks for biomedical image segmentation"
    - "unsupervised deep embedding for clustering analysis"
    - "neural network based clustering using pairwise constraints"
    - "deep clustering discriminative embeddings for segmentation and separation"
    - "deep learning with nonparametric clustering"
    - "end to end training of deep visuomotor policies"
    - "adam a method for stochastic optimization"
    - "objective criteria for the evaluation of clustering methods"

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)