human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "bayesian optimization meets riemannian manifolds in robot learning"):
  Abstract: Bayesian optimization (BO) recently became popular in robotics to optimize control parameters and parametric policies in direct reinforcement learning due to its data efficiency and gradient-free approach. However, its performance may be seriously compromised when the parameter space is high-dimensional. A way to tackle this problem is to introduce domain knowledge into the BO framework. We propose to exploit the geometry of non-Euclidean parameter spaces, which often arise in robotics (e.g. orientation, stiffness matrix). Our approach, built on Riemannian manifold theory, allows BO to properly measure similarities in the parameter space through geometry-aware kernel functions and to optimize the acquisition function on the manifold as an unconstrained problem. We test our approach in several benchmark artificial landscapes and using a 7-DOF simulated robot to learn orientation and impedance parameters for manipulation skills.

Includes members:
  Member "riemannian motion policies":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "camera to robot pose estimation from a single image")
  Member "bayesian optimization with automatic prior selection for data efficient direct policy search":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fast online adaptation in robotics through meta learning embeddings of simulated priors")
  Member "deep kernels for optimizing locomotion controllers":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "botorch programmable bayesian optimization in pytorch")
  Member "deep reinforcement learning a brief survey":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "challenges and countermeasures for adversarial attacks on deep reinforcement learning")
  Member "bayesian optimization using domain knowledge on the atrias biped":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning fast adaptation with meta strategy optimization")
  Member "on the design of lqr kernels for efficient controller learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep reinforcement learning with linear quadratic regulator regions")
  Member "pymanopt a python toolbox for optimization on manifolds using automatic differentiation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "graph prolongation convolutional networks explicitly multiscale machine learning on graphs with applications to modeling of biological systems")
  Member "learning dynamic robot to human object handover from human feedback":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "interactive trajectory adaptation through force guided bayesian optimization")

Question: Please tell me which class the source paper 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)