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: "2d linear time variant controller for human s intention detection for reach to grasp trajectories in novel scenes"
  Abstract: Designing robotic assistance devices for manipulation tasks is challenging. This work is concerned with improving accuracy and usability of semi-autonomous robots, such as human operated manipulators or exoskeletons. The key insight is to develop a system that takes into account context- and user-awareness to take better decisions in how to assist the user. The context-awareness is implemented by enabling the system to automatically generate a set of candidate grasps and reach-to-grasp trajectories in novel, cluttered scenes. The user-awareness is implemented as a linear time-variant feedback controller to facilitate the motion towards the most promising grasp. Our approach is demonstrated in a simple 2D example in which participants are asked to grasp a specific object in a clutter scene. Our approach also reduce the number of controllable dimensions for the user by providing only control on x- and y-axis, while orientation of the end-effector and the pose of its fingers are inferred by the system. The experimental results show the benefits of our approach in terms of accuracy and execution time with respect to a pure manual control.

Connected through hyperedge HE-1 (induced by source paper "aging touch systematic and unbiased presentation of tactile stimuli"):
  with members:
    - "grasping and manipulation with a multi fingered hand"
    - "metrics and benchmarks for remote shared controllers in industrial applications"
    - "automatic detection of myocontrol failures based upon situational context information"
    - "hypothesis based belief planning for dexterous grasping"
    - "feature based transfer learning for robotic push manipulation"

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)