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: "evaluating methods for end user creation of robot task plans"
  Abstract: How can we enable users to create effective, perception-driven task plans for collaborative robots? We conducted a 35-person user study with the Behavior Tree-based CoSTAR system to determine which strategies for end user creation of generalizable robot task plans are most usable and effective. CoSTAR allows domain experts to author complex, perceptually grounded task plans for collaborative robots. As a part of CoSTAR's wide range of capabilities, it allows users to specify SmartMoves: abstract goals such as "pick up component A from the right side of the table." Users were asked to perform pick-and-place assembly tasks with either SmartMoves or one of three simpler baseline versions of CoSTAR. Overall, participants found CoSTAR to be highly usable, with an average System Usability Scale score of 73.4 out of 100. SmartMove also helped users perform tasks faster and more effectively; all SmartMove users completed the first two tasks, while not all users completed the tasks using the other strategies. SmartMove users showed better performance for incorporating perception across all three tasks.

Connected through hyperedge HE-1 (induced by source paper "good robot efficient reinforcement learning for multi step visual tasks with sim to real transfer"):
  with members:
    - "6 dof grasping for target driven object manipulation in clutter"
    - "form2fit learning shape priors for generalizable assembly from disassembly"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "a review of robot learning for manipulation challenges representations and algorithms"
    - "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"
    - "playing hard exploration games by watching youtube"

Connected through hyperedge HE-2 (induced by source paper "human grasp classification for reactive human to robot handovers"):
  with members:
    - "transferable task execution from pixels through deep planning domain learning"
    - "3d hand shape and pose estimation from a single rgb image"
    - "freihand a dataset for markerless capture of hand pose and shape from single rgb images"
    - "learning joint reconstruction of hands and manipulated objects"
    - "6 dof grasping for target driven object manipulation in clutter"
    - "representing robot task plans as robust logical dynamical systems"
    - "honnotate a method for 3d annotation of hand and objects poses"
    - "online replanning in belief space for partially observable task and motion problems"

Connected through hyperedge HE-3 (induced by source paper "prospection interpretable plans from language by predicting the future"):
  with members:
    - "universal planning networks"
    - "synthetically trained neural networks for learning human readable plans from real world demonstrations"
    - "visual robot task planning"
    - "learning model based planning from scratch"
    - "neural task programming learning to generalize across hierarchical tasks"
    - "simulating action dynamics with neural process networks"
    - "imagination augmented agents for deep reinforcement learning"
    - "learning interpretable spatial operations in a rich 3d blocks world"

Connected through hyperedge HE-4 (induced by source paper "representing robot task plans as robust logical dynamical systems"):
  with members:
    - "behavior trees in robotics and ai an introduction"
    - "behavior trees as a representation for medical procedures"
    - "neural task graphs generalizing to unseen tasks from a single video demonstration"
    - "rmpflow a computational graph for automatic motion policy generation"
    - "combining neural networks and tree search for task and motion planning in challenging environments"
    - "neural task programming learning to generalize across hierarchical tasks"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "costar instructing collaborative robots with behavior trees and vision"

Connected through hyperedge HE-5 (induced by source paper "the costar block stacking dataset learning with workspace constraints"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "monocular object orientation estimation using riemannian regression and classification networks"
    - "playing hard exploration games by watching youtube"
    - "a real time robotic grasp approach with oriented anchor box"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "robustness via retrying closed loop robotic manipulation with self supervised learning"
    - "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks"
    - "shapestacks learning vision based physical intuition for generalised object stacking"

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)