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: "pixels to plans learning non prehensile manipulation by imitating a planner"
  Abstract: We present a novel method enabling robots to quickly learn to manipulate objects by leveraging a motion planner to generate "expert" training trajectories from a small amount of human-labeled data. In contrast to the traditional sense-plan-act cycle, we propose a deep learning architecture and training regimen called PtPNet that can estimate effective end-effector trajectories for manipulation directly from a single RGB-D image of an object. Additionally, we present a data collection and augmentation pipeline that enables the automatic generation of large numbers (millions) of training image and trajectory examples with almost no human labeling effort. #R##N#We demonstrate our approach in a non-prehensile tool-based manipulation task, specifically picking up shoes with a hook. In hardware experiments, PtPNet generates motion plans (open-loop trajectories) that reliably (89% success over 189 trials) pick up four very different shoes from a range of positions and orientations, and reliably picks up a shoe it has never seen before. Compared with a traditional sense-plan-act paradigm, our system has the advantages of operating on sparse information (single RGB-D frame), producing high-quality trajectories much faster than the "expert" planner (300ms versus several seconds), and generalizing effectively to previously unseen shoes.

Connected through hyperedge HE-1 (induced by source paper "robotic grasping through combined image based grasp proposal and 3d reconstruction"):
  with members:
    - "kpam sc generalizable manipulation planning using keypoint affordance and shape completion"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "kpam keypoint affordances for category level robotic manipulation"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "higher order function networks for learning composable 3d object representations"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "open3d a modern library for 3d data processing"
    - "pixel2mesh generating 3d mesh models from single rgb images"

Connected through hyperedge HE-2 (induced by source paper "higher order function networks for view planning and multi view reconstruction"):
  with members:
    - "self supervised 3d shape and viewpoint estimation from single images for robotics"
    - "higher order function networks for learning composable 3d object representations"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "atlasnet a papier m ach e approach to learning 3d surface generation"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "mask r cnn"
    - "a point set generation network for 3d object reconstruction from a single image"
    - "revisiting active perception"

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)