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: "adversarial policies attacking deep reinforcement learning"
  Abstract: Deep reinforcement learning (RL) policies are known to be vulnerable to adversarial perturbations to their observations, similar to adversarial examples for classifiers. However, an attacker is not usually able to directly modify another agent's observations. This might lead one to wonder: is it possible to attack an RL agent simply by choosing an adversarial policy acting in a multi-agent environment so as to create natural observations that are adversarial? We demonstrate the existence of adversarial policies in zero-sum games between simulated humanoid robots with proprioceptive observations, against state-of-the-art victims trained via self-play to be robust to opponents. The adversarial policies reliably win against the victims but generate seemingly random and uncoordinated behavior. We find that these policies are more successful in high-dimensional environments, and induce substantially different activations in the victim policy network than when the victim plays against a normal opponent. Videos are available at this http URL.

Connected through hyperedge HE-1 (induced by source paper "challenges and countermeasures for adversarial attacks on deep reinforcement learning"):
  with members:
    - "distilling policy distillation"
    - "sequential triggers for watermarking of deep reinforcement learning policies"
    - "deceptive reinforcement learning under adversarial manipulations on cost signals"
    - "adversarial exploitation of policy imitation"
    - "wasserstein robust reinforcement learning"
    - "rl based method for benchmarking the adversarial resilience and robustness of deep reinforcement learning policies"
    - "analysis and improvement of adversarial training in dqn agents with adversarially guided exploration age"
    - "action robust reinforcement learning and applications in continuous control"

Connected through hyperedge HE-2 (induced by source paper "scaling map elites to deep neuroevolution"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "mulex disentangling exploitation from exploration in deep rl"
    - "covariance matrix adaptation for the rapid illumination of behavior space"
    - "scheduled intrinsic drive a hierarchical take on intrinsically motivated exploration"
    - "evolvability es scalable and direct optimization of evolvability"
    - "teacher algorithms for curriculum learning of deep rl in continuously parameterized environments"
    - "reset free trial and error learning for robot damage recovery"
    - "exploration by random network distillation"

Connected through hyperedge HE-3 (induced by source paper "formulazero distributionally robust online adaptation via offline population synthesis"):
  with members:
    - "dota 2 with large scale deep reinforcement learning"
    - "alphastar an evolutionary computation perspective"
    - "online vehicle trajectory prediction using policy anticipation network and optimization based context reasoning"
    - "online control with adversarial disturbances"
    - "efficient black box assessment of autonomous vehicle safety"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "jointly learnable behavior and trajectory planning for self driving vehicles"
    - "regret bounds for robust adaptive control of the linear quadratic regulator"

Connected through hyperedge HE-4 (induced by source paper "on the robustness of cooperative multi agent reinforcement learning"):
  with members:
    - "blackbox attacks on reinforcement learning agents using approximated temporal information"
    - "the starcraft multi agent challenge"
    - "efficient communication in multi agent reinforcement learning via variance based control"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial attacks on neural network policies"
    - "tactics of adversarial attack on deep reinforcement learning agents"
    - "vulnerability of deep reinforcement learning to policy induction attacks"

Connected through hyperedge HE-5 (induced by source paper "finding and visualizing weaknesses of deep reinforcement learning agents"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "local explanation methods for deep neural networks lack sensitivity to parameter values"
    - "establishing appropriate trust via critical states"
    - "rigorous agent evaluation an adversarial approach to uncover catastrophic failures"
    - "sanity checks for saliency maps"
    - "understanding regularization to visualize convolutional neural networks"
    - "evaluating feature importance estimates"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"

Connected through hyperedge HE-6 (induced by source paper "smoothed inference for adversarially trained models"):
  with members:
    - "robust unsupervised neural machine translation with adversarial training"
    - "maxup a simple way to improve generalization of neural network training"
    - "randomized smoothing of all shapes and sizes"
    - "colored noise injection for training adversarially robust neural networks"
    - "enforcing linearity in dnn succours robustness and adversarial image generation"
    - "training robust deep neural networks via adversarial noise propagation"
    - "instance adaptive adversarial training improved accuracy tradeoffs in neural nets"
    - "certified adversarial robustness via randomized smoothing"

Connected through hyperedge HE-7 (induced by source paper "preventing imitation learning with adversarial policy ensembles"):
  with members:
    - "exploring the limitations of behavior cloning for autonomous driving"
    - "dynamics aware unsupervised discovery of skills"
    - "policy poisoning in batch reinforcement learning and control"
    - "learning novel policies for tasks"
    - "causal confusion in imitation learning"
    - "risk aware active inverse reinforcement learning"
    - "interpretable latent spaces for learning from demonstration"
    - "reinforcement learning and control as probabilistic inference tutorial and review"

Connected through hyperedge HE-8 (induced by source paper "node injection attacks on graphs via reinforcement learning"):
  with members:
    - "adversarial examples on graph data deep insights into attack and defense"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "adversarial attack on graph structured data"
    - "is feature selection secure against training data poisoning"
    - "graph transformation policy network for chemical reaction prediction"
    - "real time bidding by reinforcement learning in display advertising"
    - "semi supervised classification with graph convolutional networks"
    - "discriminative embeddings of latent variable models for structured data"

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)