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: "initial experiments on learning based randomized bin picking allowing finger contact with neighboring objects"
  Abstract: This paper proposes a novel method for randomized bin-picking based on learning. When a two-fingered gripper tries to pick an object from the pile, a finger often contacts a neighboring object. Even if a finger contacts a neighboring object, the target object will be successfully picked depending on the configuration of neighboring objects. In our proposed method, we use the visual information on neighboring objects to train the discriminator. Corresponding to a grasping posture of an object, the discriminator predicts whether or not the pick will be successful even if a finger contacts a neighboring object. We examine two learning algorithms, the linear support vector machine (SVM) and the random forest (RF) approaches. By using both methods, we demonstrate that the picking success rate is significantly higher than with conventional methods without learning.

Connected through hyperedge HE-1 (induced by source paper "learning based industrial bin picking trained with approximate physics simulator"):
  with members:
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "robotic pick and place of novel objects in clutter with multi affordance grasping and cross domain image matching"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "using geometry to detect grasps in 3d point clouds"
    - "imagenet large scale visual recognition challenge"
    - "deep learning for detecting robotic grasps"

Connected through hyperedge HE-2 (induced by source paper "experiments on learning based industrial bin picking with iterative visual recognition"):
  with members:
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "dex net 2 0 deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "iterative visual recognition for learning based randomized bin picking"
    - "using geometry to detect grasps in 3d point clouds"
    - "deep learning for detecting robotic grasps"

Connected through hyperedge HE-3 (induced by source paper "assembly sequence planning for motion planning"):
  with members:
    - "a mid level planning system for object reorientation"

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)