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: "learning to singulate objects using a push proposal network"
  Abstract: Learning to act in unstructured environments such as cluttered piles of objects poses a substantial challenge for manipulation robots. We present a novel neural network-based approach that separates unknown objects in clutter by selecting favourable push actions. Our network is trained from data collected through autonomous interaction of a PR2 robot with randomly organized tabletop scenes. The model is designed to propose meaningful push actions based on over-segmented RGB-D images. We evaluate our approach by singulating up to 8 unknown objects in clutter. We demonstrate that our method enables the robot to perform the task with a high success rate and a low number of required push actions. Our results based on real-world experiments show that our network is able to generalize to novel objects of various sizes and shapes as well as to arbitrary object configurations. Videos of our experiments can be viewed at http://robotpush.cs.uni-freiburg.de.

Connected through hyperedge HE-1 (induced by source paper "the state of service robots current bottlenecks in object perception and manipulation"):
  with members:
    - "investigating the importance of shape features color constancy color spaces and similarity measures in open ended 3d object recognition"
    - "accelerating reinforcement learning for reaching using continuous curriculum learning"
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "learning better generative models for dexterous single view grasping of novel objects"
    - "6 dof grasping for target driven object manipulation in clutter"
    - "the sound of motions"
    - "co separating sounds of visual objects"
    - "cleargrasp 3d shape estimation of transparent objects for manipulation"

Connected through hyperedge HE-2 (induced by source paper "estimating mass distribution of articulated objects through non prehensile manipulation"):
  with members:
    - "environment probing interaction policies"
    - "bounce and learn modeling scene dynamics with real world bounces"
    - "densephysnet learning dense physical object representations via multi step dynamic interactions"
    - "learning manipulation under physics constraints with visual perception"
    - "single episode policy transfer in reinforcement learning"
    - "learning kinematic descriptions using spare simulated and physical articulated extendable dataset"
    - "unsupervised learning of latent physical properties using perception prediction networks"
    - "augmenting physical simulators with stochastic neural networks case study of planar pushing and bouncing"

Connected through hyperedge HE-3 (induced by source paper "adaptive curriculum generation from demonstrations for sim to real visuomotor control"):
  with members:
    - "solving rubik s cube with a robot hand"
    - "combining learned skills and reinforcement learning for robotic manipulations"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "playing hard exploration games by watching youtube"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "robust compliant assembly via optimal belief space planning"

Connected through hyperedge HE-4 (induced by source paper "estimating mass distribution of articulated objects through physical interaction"):
  with members:
    - "environment probing interaction policies"
    - "bounce and learn modeling scene dynamics with real world bounces"
    - "densephysnet learning dense physical object representations via multi step dynamic interactions"
    - "learning manipulation under physics constraints with visual perception"
    - "learning kinematic descriptions using spare simulated and physical articulated extendable dataset"
    - "unsupervised learning of latent physical properties using perception prediction networks"
    - "augmenting physical simulators with stochastic neural networks case study of planar pushing and bouncing"
    - "flexible neural representation for physics prediction"

Connected through hyperedge HE-5 (induced by source paper "a deep learning approach to grasping the invisible"):
  with members:
    - "mechanical search multi step retrieval of a target object occluded by clutter"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "grasp2vec learning object representations from self supervised grasping"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "light weight refinenet for real time semantic segmentation"
    - "robust 6d object pose estimation in cluttered scenes using semantic segmentation and pose regression networks"
    - "hindsight experience replay"

Connected through hyperedge HE-6 (induced by source paper "gan path finder preliminary results"):
  with members:
    - "value iteration networks on multiple levels of abstraction"
    - "learning to navigate in cities without a map"
    - "gated path planning networks"
    - "improved training of wasserstein gans"
    - "cognitive mapping and planning for visual navigation"
    - "deep visual foresight for planning robot motion"
    - "target driven visual navigation in indoor scenes using deep reinforcement learning"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "learning visual dynamics models of rigid objects using relational inductive biases"):
  with members:
    - "cobra data efficient model based rl through unsupervised object discovery and curiosity driven exploration"
    - "compositional video prediction"
    - "hierarchical long term video prediction without supervision"
    - "reasoning about physical interactions with object oriented prediction and planning"
    - "relational inductive biases deep learning and graph networks"
    - "learning to generate long term future via hierarchical prediction"
    - "unsupervised learning for physical interaction through video prediction"
    - "deep visual foresight for planning robot motion"

Connected through hyperedge HE-8 (induced by source paper "multi object rearrangement with monte carlo tree search a case study on planar nonprehensile sorting"):
  with members:
    - "learning manipulation states and actions for efficient non prehensile rearrangement planning"
    - "monte carlo tree search for efficient visually guided rearrangement planning"
    - "sample efficient learning of nonprehensile manipulation policies via physics based informed state distributions"
    - "planning with a receding horizon for manipulation in clutter using a learned value function"
    - "real time online re planning for grasping under clutter and uncertainty"
    - "ffrob leveraging symbolic planning for efficient task and motion planning"
    - "randomized physics based motion planning for grasping in cluttered and uncertain environments"
    - "high quality tabletop rearrangement with overhand grasps hardness results and fast methods"

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)