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: "localizing grasp affordances in 3 d points clouds using taubin quadric fitting"
  Abstract: Perception-for-grasping is a challenging problem in robotics. Inexpensive range sensors such as the Microsoft Kinect provide sensing capabilities that have given new life to the effort of developing robust and accurate perception methods for robot grasping. This paper proposes a new approach to localizing enveloping grasp affordances in 3-D point clouds efficiently. The approach is based on modeling enveloping grasp affordances as a cylindrical shells that corresponds to the geometry of the robot hand. A fast and accurate fitting method for quadratic surfaces is the core of our approach. An evaluation on a set of cluttered environments shows high precision and recall statistics. Our results also show that the approach compares favorably with some alternatives, and that it is efficient enough to be employed for robot grasping in real-time.

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"
    - "real time 3d scene description using spheres cones and cylinders"
    - "a large dataset of object scans"
    - "difference of normals as a multi scale operator in unorganized point clouds"

Connected through hyperedge HE-2 (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"
    - "real time 3d scene description using spheres cones and cylinders"
    - "a large dataset of object scans"

Connected through hyperedge HE-3 (induced by source paper "a novel geometry based algorithm for robust grasping in extreme clutter environment"):
  with members:
    - "high precision grasp pose detection in dense clutter"
    - "deep learning a grasp function for grasping under gripper pose uncertainty"
    - "using geometry to detect grasps in 3d point clouds"
    - "supersizing self supervision learning to grasp from 50k tries and 700 robot hours"
    - "deep learning for detecting robotic grasps"

Connected through hyperedge HE-4 (induced by source paper "optimized edge based grasping method for a cluttered environment"):
  with members:
    - "high precision grasp pose detection in dense clutter"
    - "deep learning a grasp function for grasping under gripper pose uncertainty"
    - "using geometry to detect grasps in 3d point clouds"
    - "supersizing self supervision learning to grasp from 50k tries and 700 robot hours"
    - "deep learning for detecting robotic grasps"

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.RO(Robotics)