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: "mirror descent search and its acceleration"
  Abstract: Abstract   In recent years, attention has been focused on the relationship between black-box optimization problem and reinforcement learning problem. In this research, we propose the Mirror Descent Search (MDS) algorithm which is applicable both for black box optimization problems and reinforcement learning problems. Our method is based on the mirror descent method, which is a general optimization algorithm. The contribution of this research is roughly twofold. We propose two essential algorithms, called MDS and Accelerated Mirror Descent Search (AMDS), and two more approximate algorithms: Gaussian Mirror Descent Search (G-MDS) and Gaussian Accelerated Mirror Descent Search (G-AMDS). This research shows that the advanced methods developed in the context of the mirror descent research can be applied to reinforcement learning problem. We also clarify the relationship between an existing reinforcement learning algorithm and our method. With two evaluation experiments, we show our proposed algorithms converge faster than some state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "variational inference mpc for bayesian model based reinforcement learning"):
  with members:
    - "integration of imitation learning using gail and reinforcement learning using task achievement rewards via probabilistic generative model"
    - "an online learning approach to model predictive control"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "learning latent dynamics for planning from pixels"
    - "model ensemble trust region policy optimization"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "acceleration of gradient based path integral method for efficient optimal and inverse optimal control"

Connected through hyperedge HE-2 (induced by source paper "an online learning approach to model predictive control"):
  with members:
    - "introduction to online convex optimization"
    - "autorally an open platform for aggressive autonomous driving"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "vision based high speed driving with a deep dynamic observer"
    - "acceleration of gradient based path integral method for efficient optimal and inverse optimal control"
    - "adaptive online learning in dynamic environments"
    - "risk sensitive path integral control"
    - "theoretical foundation for cma es from information geometric perspective"

Connected through hyperedge HE-3 (induced by source paper "acceleration of gradient based path integral method for efficient optimal and inverse optimal control"):
  with members:
    - "information theoretic model predictive control theory and applications to autonomous driving"
    - "path integral networks end to end differentiable optimal control"
    - "adam a method for stochastic optimization"

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)