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: "costar instructing collaborative robots with behavior trees and vision"
  Abstract: For collaborative robots to become useful, end users who are not robotics experts must be able to instruct them to perform a variety of tasks. With this goal in mind, we developed a system for end-user creation of robust task plans with a broad range of capabilities. CoSTAR: the Collaborative System for Task Automation and Recognition is our winning entry in the 2016 KUKA Innovation Award competition at the Hannover Messe trade show, which this year focused on Flexible Manufacturing. CoSTAR is unique in how it creates natural abstractions that use perception to represent the world in a way users can both understand and utilize to author capable and robust task plans. Our Behavior Tree-based task editor integrates high-level information from known object segmentation and pose estimation with spatial reasoning and robot actions to create robust task plans. We describe the cross-platform design and implementation of this system on multiple industrial robots and evaluate its suitability for a wide variety of use cases.

Connected through hyperedge HE-1 (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-2 (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"
    - "evaluating methods for end user creation of robot task plans"
    - "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"

Connected through hyperedge HE-3 (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"
    - "evaluating methods for end user creation of robot task plans"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "analysis and exploitation of synchronized parallel executions in behavior trees"):
  with members:
    - "hidden markov models derived from behavior trees"
    - "behavior trees in robotics and ai an introduction"
    - "improving the modularity of auv control systems using behaviour trees"
    - "adding neural network controllers to behavior trees without destroying performance guarantees"
    - "improving the parallel execution of behavior trees"

Connected through hyperedge HE-5 (induced by source paper "hidden markov models derived from behavior trees"):
  with members:
    - "behavior trees as a representation for medical procedures"

Connected through hyperedge HE-6 (induced by source paper "sequence planner automated planning and control for ros2 based collaborative and intelligent automation systems"):
  with members:
    - "pddl2 1 an extension to pddl for expressing temporal planning domains"

Connected through hyperedge HE-7 (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"

Connected through hyperedge HE-8 (induced by source paper "visual robot task planning"):
  with members:
    - "se3 pose nets structured deep dynamics models for visuomotor planning and control"
    - "learning model based planning from scratch"
    - "combining neural networks and tree search for task and motion planning in challenging environments"
    - "neural task programming learning to generalize across hierarchical tasks"
    - "deep predictive policy training using reinforcement learning"
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"
    - "qmdp net deep learning for planning under partial observability"
    - "imagination augmented agents for deep reinforcement learning"

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)