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: "control regularization for reduced variance reinforcement learning"
  Abstract: Dealing with high variance is a significant challenge in model-free reinforcement learning (RL). Existing methods are unreliable, exhibiting high variance in performance from run to run using different initializations/seeds. Focusing on problems arising in continuous control, we propose a functional regularization approach to augmenting model-free RL. In particular, we regularize the behavior of the deep policy to be similar to a policy prior, i.e., we regularize in function space. We show that functional regularization yields a bias-variance trade-off, and propose an adaptive tuning strategy to optimize this trade-off. When the policy prior has control-theoretic stability guarantees, we further show that this regularization approximately preserves those stability guarantees throughout learning. We validate our approach empirically on a range of settings, and demonstrate significantly reduced variance, guaranteed dynamic stability, and more efficient learning than deep RL alone.

Connected through hyperedge HE-1 (induced by source paper "glas global to local safe autonomy synthesis for multi robot motion planning with end to end learning"):
  with members:
    - "trajectory generation for multiagent point to point transitions via distributed model predictive control"
    - "learning safe unlabeled multi robot planning with motion constraints"
    - "neural swarm decentralized close proximity multirotor controlusing learned interactions"
    - "graph neural networks for decentralized multi robot path planning"
    - "graph policy gradients for large scale unlabeled motion planning with constraints"
    - "pytorch an imperative style high performance deep learning library"
    - "differentiable particle filters end to end learning with algorithmic priors"

Connected through hyperedge HE-2 (induced by source paper "regularization matters in policy optimization"):
  with members:
    - "investigating generalisation in continuous deep reinforcement learning"
    - "towards robust monocular depth estimation mixing datasets for zero shot cross dataset transfer"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "generalization and regularization in dqn"
    - "meta reinforcement learning of structured exploration strategies"
    - "a study on overfitting in deep reinforcement learning"
    - "structured control nets for deep reinforcement learning"
    - "quantifying generalization in reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "imitation projected programmatic reinforcement learning"):
  with members:
    - "batch policy learning under constraints"
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "houdini lifelong learning as program synthesis"
    - "safe exploration in continuous action spaces"
    - "predictor corrector policy optimization"
    - "programmatically interpretable reinforcement learning"
    - "accelerating imitation learning with predictive models"
    - "verifiable reinforcement learning via policy extraction"

Connected through hyperedge HE-4 (induced by source paper "imitation projected policy gradient for programmatic reinforcement learning"):
  with members:
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "dual policy iteration"
    - "houdini lifelong learning as program synthesis"
    - "safe exploration in continuous action spaces"
    - "predictor corrector policy optimization"
    - "programmatically interpretable reinforcement learning"
    - "accelerating imitation learning with predictive models"
    - "fast policy learning through imitation and reinforcement"

Connected through hyperedge HE-5 (induced by source paper "robust regression for safe exploration in control"):
  with members:
    - "conditioning by adaptive sampling for robust design"
    - "episodic learning with control lyapunov functions for uncertain robotic systems"
    - "end to end safe reinforcement learning through barrier functions for safety critical continuous control tasks"
    - "stagewise safe bayesian optimization with gaussian processes"
    - "neural lander stable drone landing control using learned dynamics"
    - "spectral normalization for generative adversarial networks"
    - "measuring and regularizing networks in function space"
    - "a general safety framework for learning based control in uncertain robotic systems"

Connected through hyperedge HE-6 (induced by source paper "neural swarm decentralized close proximity multirotor controlusing learned interactions"):
  with members:
    - "robust regression for safe exploration in control"
    - "episodic learning with control lyapunov functions for uncertain robotic systems"
    - "sim to multi real transfer of low level robust control policies to multiple quadrotors"
    - "neural lander stable drone landing control using learned dynamics"
    - "residual reinforcement learning for robot control"
    - "deep sets"
    - "learn fast forget slow safe predictive learning control for systems with unknown and changing dynamics performing repetitive tasks"
    - "smooth imitation learning for online sequence prediction"

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)