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: "a probabilistic representation for dynamic movement primitives"
  Abstract: Dynamic Movement Primitives have successfully been used to realize imitation learning, trial-and-error learning, reinforce- ment learning, movement recognition and segmentation and control. Because of this they have become a popular represen- tation for motor primitives. In this work, we showcase how DMPs can be reformulated as a probabilistic linear dynamical system with control inputs. Through this probabilistic repre- sentation of DMPs, algorithms such as Kalman filtering and smoothing are directly applicable to perform inference on pro- prioceptive sensor measurements during execution. We show that inference in this probabilistic model automatically leads to a feedback term to online modulate the execution of a DMP. Furthermore, we show how inference allows us to measure the likelihood that we are successfully executing a given motion primitive. In this context, we show initial results of using the probabilistic model to detect execution failures on a simulated movement primitive dataset.

Connected through hyperedge HE-1 (induced by source paper "neural probabilistic motor primitives for humanoid control"):
  with members:
    - "hierarchical visuomotor control of humanoids"
    - "knowledge transfer with jacobian matching"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "emergent complexity via multi agent competition"
    - "robust imitation of diverse behaviors"
    - "dart noise injection for robust imitation learning"
    - "learning human behaviors from motion capture by adversarial imitation"
    - "emergence of locomotion behaviours in rich environments"

Connected through hyperedge HE-2 (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"
    - "learning human robot collaboration insights through the integration of muscle activity in interaction motion models"
    - "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"
    - "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)