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: "reinforcement learning in rich observation mdps using spectral methods"
  Abstract: Reinforcement learning (RL) in Markov decision processes (MDPs) with large state spaces is a challenging problem. The performance of standard RL algorithms degrades drastically with the dimensionality of state space. However, in practice, these large MDPs typically incorporate a latent or hidden low-dimensional structure. In this paper, we study the setting of rich-observation Markov decision processes (ROMDP), where there are a small number of hidden states which possess an injective mapping to the observation states. In other words, every observation state is generated through a single hidden state, and this mapping is unknown a priori. We introduce a spectral decomposition method that consistently learns this mapping, and more importantly, achieves it with low regret. The estimated mapping is integrated into an optimistic RL algorithm (UCRL), which operates on the estimated hidden space. We derive finite-time regret bounds for our algorithm with a weak dependence on the dimensionality of the observed space. In fact, our algorithm asymptotically achieves the same average regret as the oracle UCRL algorithm, which has the knowledge of the mapping from hidden to observed spaces. Thus, we derive an efficient spectral RL algorithm for ROMDPs.

Connected through hyperedge HE-1 (induced by source paper "can agents learn by analogy an inferable model for pac reinforcement learning"):
  with members:
    - "provably efficient reinforcement learning with linear function approximation"
    - "sample optimal parametric q learning using linearly additive features"
    - "reinforcement leaning in feature space matrix bandit kernels and regret bound"
    - "sample optimal parametric q learning with linear transition models"
    - "policy certificates towards accountable reinforcement learning"
    - "unifying pac and regret uniform pac bounds for episodic reinforcement learning"
    - "reinforcement learning of pomdp s using spectral methods"
    - "reinforcement learning of pomdps using spectral methods"

Connected through hyperedge HE-2 (induced by source paper "provably efficient rl with rich observations via latent state decoding"):
  with members:
    - "is q learning provably efficient"
    - "value prediction network"
    - "count based exploration with neural density models"
    - "curiosity driven exploration by self supervised prediction"
    - "deep exploration via bootstrapped dqn"
    - "reinforcement learning of pomdps using spectral methods"
    - "contextual decision processes with low bellman rank are pac learnable"
    - "pac reinforcement learning with rich observations"

Connected through hyperedge HE-3 (induced by source paper "pac reinforcement learning without real world feedback"):
  with members:
    - "provably efficient rl with rich observations via latent state decoding"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "on oracle efficient pac rl with rich observations"
    - "model based rl in contextual decision processes pac bounds and exponential improvements over model free approaches"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "contextual decision processes with low bellman rank are pac learnable"

Connected through hyperedge HE-4 (induced by source paper "sample optimal parametric q learning using linearly additive features"):
  with members:
    - "state aggregation learning from markov transition data"
    - "scalable bilinear pi learning using state and action features"
    - "variance reduced value iteration and faster algorithms for solving markov decision processes"
    - "contextual decision processes with low bellman rank are pac learnable"
    - "learning topic models going beyond svd"

Connected through hyperedge HE-5 (induced by source paper "efficient exploration through bayesian deep q networks"):
  with members:
    - "stochastic activation pruning for robust adversarial defense"
    - "deep reinforcement learning that matters"
    - "equivalence between policy gradients and soft q learning"
    - "shallow updates for deep reinforcement learning"
    - "count based exploration with neural density models"
    - "noisy networks for exploration"
    - "combating deep reinforcement learning s sisyphean curse with intrinsic fear"
    - "revisiting the arcade learning environment evaluation protocols and open problems for general agents"

Connected through hyperedge HE-6 (induced by source paper "surprising negative results for generative adversarial tree search"):
  with members:
    - "world models"
    - "spectral normalization for generative adversarial networks"
    - "the effect of planning shape on dyna style planning in high dimensional state spaces"
    - "efficient exploration through bayesian deep q networks"
    - "value prediction network"
    - "count based exploration with neural density models"
    - "improved training of wasserstein gans"
    - "noisy networks for exploration"

Connected through hyperedge HE-7 (induced by source paper "on oracle efficient pac rl with rich observations"):
  with members:
    - "sbeed convergent reinforcement learning with nonlinear function approximation"
    - "unifying pac and regret uniform pac bounds for episodic reinforcement learning"
    - "a pac rl algorithm for episodic pomdps"
    - "reinforcement learning of pomdps using spectral methods"
    - "contextual decision processes with low bellman rank are pac learnable"
    - "pac reinforcement learning with rich observations"
    - "learning to search better than your teacher"
    - "reinforcement and imitation learning via interactive no regret learning"

Connected through hyperedge HE-8 (induced by source paper "on polynomial time pac reinforcement learning with rich observations"):
  with members:
    - "unifying pac and regret uniform pac bounds for episodic reinforcement learning"
    - "a pac rl algorithm for episodic pomdps"
    - "reinforcement learning of pomdps using spectral methods"
    - "contextual decision processes with low bellman rank are pac learnable"
    - "pac reinforcement learning with rich observations"
    - "learning to search better than your teacher"
    - "reinforcement and imitation learning via interactive no regret learning"
    - "taming the monster a fast and simple algorithm for contextual bandits"

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.AI(Artificial Intelligence)