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: "curiosity driven exploration in deep reinforcement learning via bayesian neural networks"
  Abstract: Scalable and effective exploration remains a key challenge in reinforcement learning (RL). While there are methods with optimality guarantees in the setting of discrete state and action spaces, these methods cannot be applied in high-dimensional deep RL scenarios. As such, most contemporary RL relies on simple heuristics such as epsilon-greedy exploration or adding Gaussian noise to the controls. This paper introduces Variational Information Maximizing Exploration (VIME), an exploration strategy based on maximization of information gain about the agent's belief of environment dynamics. We propose a practical implementation, using variational inference in Bayesian neural networks which efficiently handles continuous state and action spaces. VIME modifies the MDP reward function, and can be applied with several different underlying RL algorithms. We demonstrate that VIME achieves significantly better performance compared to heuristic exploration methods across a variety of continuous control tasks and algorithms, including tasks with very sparse rewards.

Connected through hyperedge HE-1 (induced by source paper "scalable quantitative verification for deep neural networks"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "quantitative verification of neural networks and its security applications"
    - "on evaluating adversarial robustness"
    - "a dual approach to scalable verification of deep networks"
    - "stochastic activation pruning for robust adversarial defense"
    - "concolic testing for deep neural networks"
    - "certified defenses against adversarial examples"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

Connected through hyperedge HE-2 (induced by source paper "dynamics aware unsupervised discovery of skills"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "therml the thermodynamics of machine learning"
    - "model ensemble trust region policy optimization"
    - "soft actor critic algorithms and applications"
    - "diversity is all you need learning skills without a reward function"
    - "unsupervised control through non parametric discriminative rewards"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"

Connected through hyperedge HE-3 (induced by source paper "neural embedding for physical manipulations"):
  with members:
    - "mcp learning composable hierarchical control with multiplicative compositional policies"
    - "normalized diversification"
    - "interpretable latent spaces for learning from demonstration"
    - "hierarchical visuomotor control of humanoids"
    - "spectral normalization for generative adversarial networks"
    - "latent space policies for hierarchical reinforcement learning"
    - "diversity is all you need learning skills without a reward function"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"

Connected through hyperedge HE-4 (induced by source paper "a comprehensive guide to bayesian convolutional neural network with variational inference"):
  with members:
    - "uncertainty estimations by softplus normalization in bayesian convolutional neural networks with variational inference"
    - "exploring sparsity in recurrent neural networks"
    - "the power of sparsity in convolutional neural networks"
    - "bayesian recurrent neural networks"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "stochastic backpropagation through mixture density distributions"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "efficient exploration for dialog policy learning with deep bbq networks replay buffer spiking"

Connected through hyperedge HE-5 (induced by source paper "deep active inference as variational policy gradients"):
  with members:
    - "bayesian policy selection using active inference"
    - "virel a variational inference framework for reinforcement learning"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "expanding the active inference landscape more intrinsic motivations in the perception action loop"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "unsupervised predictive memory in a goal directed agent"
    - "recurrent world models facilitate policy evolution"

Connected through hyperedge HE-6 (induced by source paper "adaptive variance for changing sparse reward environments"):
  with members:
    - "learning dexterous in hand manipulation"
    - "the uncertainty bellman equation and exploration"
    - "learning complex dexterous manipulation with deep reinforcement learning and demonstrations"
    - "surprise based intrinsic motivation for deep reinforcement learning"
    - "continuous adaptation via meta learning in nonstationary and competitive environments"
    - "preparing for the unknown learning a universal policy with online system identification"
    - "bridging the gap between value and policy based reinforcement learning"
    - "reinforcement learning with deep energy based policies"

Connected through hyperedge HE-7 (induced by source paper "learning driven exploration for reinforcement learning"):
  with members:
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "surprise based intrinsic motivation for deep reinforcement learning"
    - "proximal policy optimization algorithms"
    - "vizdoom a doom based ai research platform for visual reinforcement learning"
    - "deep exploration via bootstrapped dqn"
    - "exploration a study of count based exploration for deep reinforcement learning"
    - "unifying count based exploration and intrinsic motivation"
    - "taming the noise in reinforcement learning via soft updates"

Connected through hyperedge HE-8 (induced by source paper "a meta mdp approach to exploration for lifelong reinforcement learning"):
  with members:
    - "reinforcement learning for uav attitude control"
    - "multi advisor reinforcement learning"
    - "routing networks adaptive selection of non linear functions for multi task learning"
    - "hybrid reward architecture for reinforcement learning"
    - "count based exploration in feature space for reinforcement learning"
    - "curiosity driven exploration by self supervised prediction"
    - "proximal policy optimization algorithms"
    - "model agnostic meta learning for fast adaptation of deep 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)