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: "adaptation and robust learning of probabilistic movement primitives"
  Abstract: Probabilistic representations of movement primitives open important new possibilities for machine learning in robotics. These representations are able to capture the variability of the demonstrations from a teacher as a probability distribution over trajectories, providing a sensible region of exploration and the ability to adapt to changes in the robot environment. However, to be able to capture variability and correlations between different joints, a probabilistic movement primitive requires the estimation of a larger number of parameters compared to their deterministic counterparts, that focus on modeling only the mean behavior. In this paper, we make use of prior distributions over the parameters of a probabilistic movement primitive to make robust estimates of the parameters with few training instances. In addition, we introduce general purpose operators to adapt movement primitives in joint and task space. The proposed training method and adaptation operators are tested in a coffee preparation and in robot table tennis task. In the coffee preparation task we evaluate the generalization performance to changes in the location of the coffee grinder and brewing chamber in a target area, achieving the desired behavior after only two demonstrations. In the table tennis task we evaluate the hit and return rates, outperforming previous approaches while using fewer task specific heuristics.

Connected through hyperedge HE-1 (induced by source paper "active learning of probabilistic movement primitives"):
  with members:
    - "relaxed rigidity constraints kinematic trajectory optimization and collision avoidance for in grasp manipulation"
    - "probabilistic object tracking using a range camera"
    - "a compliant underactuated hand for robust manipulation"
    - "active learning of inverse models with intrinsically motivated goal exploration in robots"
    - "active imitation learning via reduction to i i d active learning"
    - "interactive policy learning through confidence based autonomy"

Connected through hyperedge HE-2 (induced by source paper "real time trajectory prediction using deep conditional generative models"):
  with members:
    - "reliable real time ball tracking for robot table tennis"
    - "convolutional neural network for trajectory prediction"
    - "long term forecasting using higher order tensor rnns"
    - "wavenet a generative model for raw audio"
    - "pixel recurrent neural networks"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-3 (induced by source paper "generalized multiple correlation coefficient as a similarity measurements between trajectories"):
  with members:
    - "an algorithmic perspective on imitation learning"
    - "time warp edit distance"

Connected through hyperedge HE-4 (induced by source paper "reliable real time ball tracking for robot table tennis"):
  with members:
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "full resolution residual networks for semantic segmentation in street scenes"

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.LG(Machine Learning)