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: "real time 3d scene description using spheres cones and cylinders"
  Abstract: The paper describes a novel real-time algorithm for finding 3D geometric primitives (cylinders, cones and spheres) from 3D range data. In its core, it performs a fast model fitting with a model update in constant time (O(1)) for each new data point added to the model. We use a three stage approach.The first step inspects 1.5D sub spaces, to find ellipses. The next stage uses these ellipses as input by examining their neighborhood structure to form sets of candidates for the 3D geometric primitives. Finally, candidate ellipses are fitted to the geometric primitives. The complexity for point processing is O(n); additional time of lower order is needed for working on significantly smaller amount of mid-level objects. This allows the approach to process 30 frames per second on Kinect depth data, which suggests this approach as a pre-processing step for 3D real-time higher level tasks in robotics, like tracking or feature based mapping.

Connected through hyperedge HE-1 (induced by source paper "generic primitive detection in point clouds using novel minimal quadric fits"):
  with members:
    - "ppfnet global context aware local features for robust 3d point matching"
    - "pcpnet learning local shape properties from raw point clouds"
    - "3d prnn generating shape primitives with recurrent neural networks"
    - "grasping unknown objects in clutter by superquadric representation"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "a large dataset of object scans"
    - "localizing grasp affordances in 3 d points clouds using taubin quadric fitting"
    - "difference of normals as a multi scale operator in unorganized point clouds"

Connected through hyperedge HE-2 (induced by source paper "primitive fitting using deep boundary aware geometric segmentation"):
  with members:
    - "geometric multi model fitting with a convex relaxation algorithm"
    - "multi class model fitting by energy minimization and mode seeking"
    - "casenet deep category aware semantic edge detection"
    - "de noising stabilizing and completing 3d reconstructions on the go using plane priors"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "deep watershed transform for instance segmentation"
    - "deep residual learning for image recognition"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-3 (induced by source paper "a minimalist approach to type agnostic detection of quadrics in point clouds"):
  with members:
    - "ppfnet global context aware local features for robust 3d point matching"
    - "sparse geometric representation through local shape probing"
    - "grasping unknown objects in clutter by superquadric representation"
    - "cad priors for accurate and flexible instance reconstruction"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "a large dataset of object scans"
    - "localizing grasp affordances in 3 d points clouds using taubin quadric fitting"

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)