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: "learning data augmentation policies using augmented random search"
  Abstract: Previous attempts for data augmentation are designed manually, and the augmentation policies are dataset-specific. Recently, an automatic data augmentation approach, named AutoAugment, is proposed using reinforcement learning. AutoAugment searches for the augmentation polices in the discrete search space, which may lead to a sub-optimal solution. In this paper, we employ the Augmented Random Search method (ARS) to improve the performance of AutoAugment. Our key contribution is to change the discrete search space to continuous space, which will improve the searching performance and maintain the diversities between sub-policies. With the proposed method, state-of-the-art accuracies are achieved on CIFAR-10, CIFAR-100, and ImageNet (without additional data). Our code is available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "simulating content consistent vehicle datasets with attribute descent"):
  with members:
    - "meta sim learning to generate synthetic datasets"
    - "vehicle re identification an efficient baseline using triplet embedding"
    - "cityflow a city scale benchmark for multi target multi camera vehicle tracking and re identification"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "moment matching for multi source domain adaptation"
    - "ram a region aware deep model for vehicle re identification"
    - "learning to simulate"
    - "unity a general platform for intelligent agents"

Connected through hyperedge HE-2 (induced by source paper "augmented random search for quadcopter control an alternative to reinforcement learning"):
  with members:
    - "simple random search provides a competitive approach to reinforcement learning"
    - "reinforcement learning for uav attitude control"
    - "deep reinforcement learning that matters"
    - "reproducibility of benchmarked deep reinforcement learning tasks for continuous control"
    - "guiding the search in continuous state action spaces by learning an action sampling distribution from off target samples"
    - "least squares temporal difference learning for the linear quadratic regulator"
    - "towards generalization and simplicity in continuous control"
    - "emergence of locomotion behaviours in rich environments"

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)