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: "online meta learning by parallel algorithm competition"
  Abstract: The efficiency of reinforcement learning algorithms depends critically on a few meta-parameters that modulates the learning updates and the trade-off between exploration and exploitation. The adaptation of the meta-parameters is an open question in reinforcement learning, which arguably has become more of an issue recently with the success of deep reinforcement learning in high-dimensional state spaces. The long learning times in domains such as Atari 2600 video games makes it not feasible to perform comprehensive searches of appropriate meta-parameter values. We propose the Online Meta-learning by Parallel Algorithm Competition (OMPAC) method. In the OMPAC method, several instances of a reinforcement learning algorithm are run in parallel with small differences in the initial values of the meta-parameters. After a fixed number of episodes, the instances are selected based on their performance in the task at hand. Before continuing the learning, Gaussian noise is added to the meta-parameters with a predefined probability. We validate the OMPAC method by improving the state-of-the-art results in stochastic SZ-Tetris and in standard Tetris with a smaller, 10$\times$10, board, by 31% and 84%, respectively, and by improving the results for deep Sarsa($\lambda$) agents in three Atari 2600 games by 62% or more. The experiments also show the ability of the OMPAC method to adapt the meta-parameters according to the learning progress in different tasks.

Connected through hyperedge HE-1 (induced by source paper "one shot bayes opt with probabilistic population based training"):
  with members:
    - "a generalized framework for population based training"
    - "emergent coordination through competition"
    - "towards explaining the regularization effect of initial large learning rate in training neural networks"
    - "fast efficient hyperparameter tuning for policy gradients"
    - "bayesian optimization under heavy tailed payoffs"
    - "massively parallel hyperparameter tuning"
    - "bayesian optimization in alphago"
    - "tune a research platform for distributed model selection and training"

Connected through hyperedge HE-2 (induced by source paper "online meta learning"):
  with members:
    - "learning to learn stochastic gradient descent with biased regularization"
    - "growing a brain fine tuning by increasing model capacity"
    - "introduction to online convex optimization"
    - "provable guarantees for gradient based meta learning"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "truncated back propagation for bilevel optimization"
    - "how to train your maml"
    - "plan online learn offline efficient learning and exploration via model based control"

Connected through hyperedge HE-3 (induced by source paper "meta gradient reinforcement learning"):
  with members:
    - "recasting gradient based meta learning as hierarchical bayes"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "on learning intrinsic rewards for policy gradient methods"
    - "rainbow combining improvements in deep reinforcement learning"
    - "one shot visual imitation learning via meta learning"
    - "a distributional perspective on reinforcement learning"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "meta learning and universality deep representations and gradient descent can approximate any learning algorithm"

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)