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: "cartman the low cost cartesian manipulator that won the amazon robotics challenge"
  Abstract: The Amazon Robotics Challenge enlisted sixteen teams to each design a pick-and-place robot for autonomous warehousing, addressing development in robotic vision and manipulation. This paper presents the design of our custom-built, cost-effective, Cartesian robot system Cartman, which won first place in the competition finals by stowing 14 (out of 16) and picking all 9 items in 27 minutes, scoring a total of 272 points. We highlight our experience-centred design methodology and key aspects of our system that contributed to our competitiveness. We believe these aspects are crucial to building robust and effective robotic systems.

Connected through hyperedge HE-1 (induced by source paper "pointins point based instance segmentation"):
  with members:
    - "deep snake for real time instance segmentation"
    - "embedmask embedding coupling for one stage instance segmentation"
    - "polarmask single shot instance segmentation with polar representation"
    - "mask scoring r cnn"
    - "tensormask a foundation for dense object segmentation"
    - "hybrid task cascade for instance segmentation"
    - "fcos fully convolutional one stage object detection"
    - "yolact real time instance segmentation"

Connected through hyperedge HE-2 (induced by source paper "toward sim to real directional semantic grasping"):
  with members:
    - "mechanical search multi step retrieval of a target object occluded by clutter"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "learning task oriented grasping for tool manipulation from simulated self supervision"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "unity a general platform for intelligent agents"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "adversarial discriminative sim to real transfer of visuo motor policies"

Connected through hyperedge HE-3 (induced by source paper "rlbench the robot learning benchmark learning environment"):
  with members:
    - "pyrep bringing v rep to deep robot learning"
    - "benchmarking simulated robotic manipulation through a real world dataset"
    - "meta world a benchmark and evaluation for multi task and meta reinforcement learning"
    - "learning agile and dynamic motor skills for legged robots"
    - "simitate a hybrid imitation learning benchmark"
    - "robonet large scale multi robot learning"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "deepmind control suite"

Connected through hyperedge HE-4 (induced by source paper "directional semantic grasping of real world objects from simulation to reality"):
  with members:
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "learning task oriented grasping for tool manipulation from simulated self supervision"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "unity a general platform for intelligent agents"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "adversarial discriminative sim to real transfer of visuo motor policies"
    - "multi task domain adaptation for deep learning of instance grasping from simulation"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

Connected through hyperedge HE-5 (induced by source paper "tossingbot learning to throw arbitrary objects with residual physics"):
  with members:
    - "residual policy learning"
    - "data augmented contact model for rigid body simulation"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "augmenting physical simulators with stochastic neural networks case study of planar pushing and bouncing"
    - "residual reinforcement learning for robot control"
    - "combining learned and analytical models for predicting action effects"
    - "deep predictive policy training using reinforcement learning"
    - "learning data efficient rigid body contact models case study of planar impact"

Connected through hyperedge HE-6 (induced by source paper "vision based picking system for automatic express package dispatching"):
  with members:
    - "grasp pose detection in point clouds"
    - "design of a multi modal end effector and grasping system how integrated design helped win the amazon robotics challenge"
    - "dex net 3 0 computing robust robot suction grasp targets in point clouds using a new analytic model and deep learning"
    - "robotic pick and place of novel objects in clutter with multi affordance grasping and cross domain image matching"
    - "high precision grasp pose detection in dense clutter"
    - "yolo9000 better faster stronger"
    - "a summary of team mit s approach to the amazon picking challenge 2015"
    - "multi view self supervised deep learning for 6d pose estimation in the amazon picking challenge"

Connected through hyperedge HE-7 (induced by source paper "towards robust product packing with a minimalistic end effector"):
  with members:
    - "robust 6d object pose estimation with stochastic congruent sets"
    - "fast object learning and dual arm coordination for cluttered stowing picking and packing"
    - "a self supervised learning system for object detection using physics simulation and multi view pose estimation"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "improving 6d pose estimation of objects in clutter via physics aware monte carlo tree search"
    - "mask r cnn"
    - "analysis and observations from the first amazon picking challenge"
    - "global hypothesis generation for 6d object pose estimation"

Connected through hyperedge HE-8 (induced by source paper "pyrep bringing v rep to deep robot learning"):
  with members:
    - "sim to real reinforcement learning for deformable object manipulation"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "transferring end to end visuomotor control from simulation to real world for a multi stage task"

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)