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: "occlusion edge detection in rgb d frames using deep convolutional networks"
  Abstract: BSTRACTOcclusion edges in images which correspond to range discontinuity in the scenefrom the point of view of the observer are an important prerequisite for manyvision and mobile robot tasks. Although they can be extracted from range datahowever extracting them from images and videos would be extremely beneﬁcial.We trained a deep convolutional neural network (CNN) to identify occlusion edgesin images and videos with both RGB-D and RGB inputs. The use of CNN avoidshand-crafting of features for automatically isolating occlusion edges and distin-guishing them from appearance edges. Other than quantitative occlusion edgedetection results, qualitative results are provided to demonstrate the trade-off be-tween high resolution analysis and frame-level computation time which is criticalfor real-time robotics applications.

Connected through hyperedge HE-1 (induced by source paper "learning localized geometric features using 3d cnn an application to manufacturability analysis of drilled holes"):
  with members:
    - "recursive training of 2d 3d convolutional networks for neuronal boundary detection"
    - "deep value of information estimators for collaborative human machine information gathering"
    - "predicting alzheimer s disease a neuroimaging study with 3d convolutional neural networks"
    - "learning spatiotemporal features with 3d convolutional networks"

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)