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: "high dimensional motion planning using latent variable models via approximate inference"
  Abstract: In this work, we present an efficient framework to generate a motion trajectory of a robot that has a high degree of freedom (e.g., a humanoid robot). High-dimensionality of the robot configuration space often leads to difficulties in utilizing the widely-used motion planning algorithms because the volume of the decision space increases exponentially with the number of dimensions. To handle complications arising from the large decision space, and to solve a corresponding motion planning problem efficiently, two key concepts were adopted. First, the Gaussian process latent variable model (GP-LVM) was utilized for low-dimensional representation of the original configuration space. Second, an approximate inference algorithm was used, exploiting through the duality between control and estimation, to explore the decision space and to compute a high-quality motion trajectory of the robot. Utilizing the GP-LVM and the duality between control and estimation, we construct a fully probabilistic generative model with which we transform a high-dimensional motion planning problem into a tractable inference problem. Finally, we compute the optimal motion trajectory via an approximate inference algorithm based on a variant of the particle filter. Numerical experiments are presented to demonstrate the applicability and validity of the proposed approach. The resulting motions can be viewed in the supplemental video. ( this https URL )

Connected through hyperedge HE-1 (induced by source paper "robot motion planning in learned latent spaces"):
  with members:
    - "universal planning networks"
    - "motion planning networks"
    - "self consistent trajectory autoencoder hierarchical reinforcement learning with trajectory embeddings"
    - "learning sampling distributions for robot motion planning"
    - "robust locally linear controllable embedding"
    - "multimodal probabilistic model based planning for human robot interaction"
    - "deep visual foresight for planning robot motion"
    - "embed to control a locally linear latent dynamics model for control from raw images"

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)