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: "risk averse robust adversarial reinforcement learning"
  Abstract: Deep reinforcement learning has recently made significant progress in solving computer games and robotic control tasks. A known problem, though, is that policies overfit to the training environment and may not avoid rare, catastrophic events such as automotive accidents. A classical technique for improving the robustness of reinforcement learning algorithms is to train on a set of randomized environments, but this approach only guards against common situations. Recently, robust adversarial reinforcement learning (RARL) was developed, which allows efficient applications of random and systematic perturbations by a trained adversary. A limitation of RARL is that only the expected control objective is optimized; there is no explicit modeling or optimization of risk. Thus the agents do not consider the probability of catastrophic events (i.e., those inducing abnormally large negative reward), except through their effect on the expected objective. In this paper we introduce risk-averse robust adversarial reinforcement learning (RARARL), using a risk-averse protagonist and a risk-seeking adversary. We test our approach on a self-driving vehicle controller. We use an ensemble of policy networks to model risk as the variance of value functions. We show through experiments that a risk-averse agent is better equipped to handle a risk-seeking adversary, and experiences substantially fewer crashes compared to agents trained without an adversary.

Connected through hyperedge HE-1 (induced by source paper "improving generalization of reinforcement learning with minimax distributional soft actor critic"):
  with members:
    - "addressing value estimation errors in reinforcement learning with a state action return distribution function"
    - "investigating generalisation in continuous deep reinforcement learning"
    - "centralized conflict free cooperation for connected and automated vehicles at intersections by proximal policy optimization"
    - "a comparative analysis of expected and distributional reinforcement learning"
    - "composable deep reinforcement learning for robotic manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "risk sensitive reinforcement learning a constrained optimization viewpoint"
    - "assessing generalization in deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "deep adversarial reinforcement learning for object disentangling"):
  with members:
    - "solving rubik s cube with a robot hand"
    - "dota 2 with large scale deep reinforcement learning"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "adversarial attacks on neural network policies"
    - "automatic goal generation for reinforcement learning agents"
    - "robust deep reinforcement learning with adversarial attacks"
    - "robust adversarial reinforcement learning"
    - "reverse curriculum generation for reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "how you act tells a lot privacy leakage attack on deep reinforcement learning"):
  with members:
    - "reinforcement learning from imperfect demonstrations"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "deep reinforcement learning that matters"
    - "virtual to real reinforcement learning for autonomous driving"
    - "attention is all you need"
    - "robust adversarial reinforcement learning"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "machine learning models that remember too much"

Connected through hyperedge HE-4 (induced by source paper "efficient and robust reinforcement learning with uncertainty based value expansion"):
  with members:
    - "artificial intelligence for prosthetics challenge solutions"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "implicit quantile networks for distributional reinforcement learning"
    - "model based value estimation for efficient model free reinforcement learning"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "value prediction network"
    - "learning to run with actor critic ensemble"

Connected through hyperedge HE-5 (induced by source paper "characterizing attacks on deep reinforcement learning"):
  with members:
    - "semanticadv generating adversarial examples via attribute conditional image editing"
    - "meshadv adversarial meshes for visual recognition"
    - "how you act tells a lot privacy leakage attack on deep reinforcement learning"
    - "spatially transformed adversarial examples"
    - "assessing generalization in deep reinforcement learning"
    - "characterizing adversarial examples based on spatial consistency information for semantic segmentation"
    - "exploring the space of black box attacks on deep neural networks"
    - "generating adversarial examples with adversarial networks"

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)