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: "grasping unknown objects in clutter by superquadric representation"
  Abstract: In this paper, a quick and efficient method is presented for grasping unknown objects in clutter. The grasping method relies on real-time superquadric (SQ) representation of partial view objects and incomplete object modelling, well suited for unknown symmetric objects in cluttered scenarios which is followed by optimized antipodal grasping. The incomplete object models are processed through a mirroring algorithm that assumes symmetry to first create an approximate complete model and then fit for SQ representation. The grasping algorithm is designed for maximum force balance and stability, taking advantage of the quick retrieval of dimension and surface curvature information from the SQ parameters. The pose of the SQs with respect to the direction of gravity is calculated and used together with the parameters of the SQs and specification of the gripper, to select the best direction of approach and contact points. The SQ fitting method has been tested on custom datasets containing objects in isolation as well as in clutter. The grasping algorithm is evaluated on a PR2 and real time results are presented. Initial results indicate that though the method is based on simplistic shape information, it outperforms other learning based grasping algorithms that also work in clutter in terms of time-efficiency and accuracy.

Connected through hyperedge HE-1 (induced by source paper "dgcm net dense geometrical correspondence matching network for incremental experience based robotic grasping"):
  with members:
    - "directional semantic grasping of real world objects from simulation to reality"
    - "pix2pose pixel wise coordinate regression of objects for 6d pose estimation"
    - "dpod 6d pose object detector and refiner"
    - "metagrasp data efficient grasping by affordance interpreter network"
    - "kpam keypoint affordances for category level robotic manipulation"
    - "normalized object coordinate space for category level 6d object pose and size estimation"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "6 dof graspnet variational grasp generation for object manipulation"

Connected through hyperedge HE-2 (induced by source paper "6 dof grasping for target driven object manipulation in clutter"):
  with members:
    - "planning multi fingered grasps as probabilistic inference in a learned deep network"
    - "mechanical search multi step retrieval of a target object occluded by clutter"
    - "the best of both modes separately leveraging rgb and depth for unseen object instance segmentation"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "pointnetgpd detecting grasp configurations from point sets"
    - "real time online re planning for grasping under clutter and uncertainty"
    - "robot learning in homes improving generalization and reducing dataset bias"

Connected through hyperedge HE-3 (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"
    - "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"
    - "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-4 (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"
    - "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"
    - "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.RO(Robotics)