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 human robot collaboration insights through the integration of muscle activity in interaction motion models"
  Abstract: Recent progress in human-robot collaboration (HRC) makes fast and fluid interactions possible. Methods like Interaction Probabilistic Movement Primitives (ProMPs) model human motion trajectories through motion capture systems. However, such presentation does not properly model tasks where the motion trajectories are similar. We propose to integrate the Electromyography (EMG) signals into the Interaction ProMPs framework. The contribution of this paper is the increased capacity to discern tasks that have similar trajectories but ones in which different tools are utilized and require the robot to adjust its pose for proper handling. Augmented Interaction ProMPs are used with an augmented vector involving muscle activity. Augmented trajectories are used to learn correlation parameters and robot motions are generated by finding a best fit task. Collaborative task scenarios with similar motions but different objects were used and compared. Integrating EMG signals into collaborative tasks significantly increases the ability to recognize nuances in the tasks.

Connected through hyperedge HE-1 (induced by source paper "endowing robots with longer term autonomy by recovering from external disturbances in manipulation through grounded anomaly classification and recovery policies"):
  with members:
    - "composable deep reinforcement learning for robotic manipulation"
    - "identifying object states in cooking related images"
    - "online robot introspection via wrench based action grammars"
    - "robot introspection with bayesian nonparametric vector autoregressive hidden markov models"
    - "fast robust and versatile event detection through hmm belief state gradient measures"
    - "functional object oriented network for manipulation learning"
    - "a probabilistic representation for dynamic movement primitives"
    - "learning modular neural network policies for multi task and multi robot transfer"

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)