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: "contextual mdps for pac reinforcement learning with rich observations"
  Abstract: We propose and study a new tractable model for reinforcement learning with rich observations called Contextual-MDPs, generalizing contextual bandits to sequential decision making. These models require an agent to take actions based on observations (features) with the goal of achieving long-term performance competitive with a large set of policies. To avoid barriers to sample-efficient learning associated with large observation spaces and general POMDPs, Contextual-MDPs can be summarized by a small number of hidden states and long-term rewards are predictable by a reactive function class. In this setting, we design a new reinforcement learning algorithm that engages in global exploration and analyze its sample complexity. We prove that the algorithm learns near optimal behavior after a number of episodes that is polynomial in all relevant parameters, logarithmic in the number of policies, and independent of the size of the observation space. This represents an exponential improvement over all existing alternative approaches and provides theoretical justification for reinforcement learning with function approximation.

Connected through hyperedge HE-1 (induced by source paper "autonomous aerial cinematography in unstructured environments with learned artistic decision making"):
  with members:
    - "improved generalization of heading direction estimation for aerial filming using semi supervised regression"
    - "can a robot become a movie director learning artistic principles for aerial cinematography"
    - "towards a robust aerial cinematography platform localizing and tracking moving targets in unstructured environments"
    - "autonomous drone cinematographer using artistic principles to create smooth safe occlusion free trajectories for aerial filming"
    - "integrating kinematics and environment context into deep inverse reinforcement learning for predicting off road vehicle trajectories"
    - "optimizing for aesthetically pleasing quadrotor camera motion"
    - "good semi supervised learning that requires a bad gan"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-2 (induced by source paper "experimental results reinforcement learning of pomdps using spectral methods"):
  with members:
    - "reinforcement learning of pomdp s using spectral methods"
    - "open problem approximate planning of pomdps in the class of memoryless policies"
    - "efficient learning and planning with compressed predictive states"
    - "nonparametric estimation of multi view latent variable models"
    - "playing atari with deep reinforcement learning"
    - "a method of moments for mixture models and hidden markov models"
    - "closing the learning planning loop with predictive state representations"

Connected through hyperedge HE-3 (induced by source paper "reinforcement learning in rich observation mdps using spectral methods"):
  with members:
    - "experimental results reinforcement learning of pomdps using spectral methods"
    - "unifying pac and regret uniform pac bounds for episodic reinforcement learning"
    - "reinforcement learning of pomdp s using spectral methods"
    - "a pac rl algorithm for episodic pomdps"
    - "open problem approximate planning of pomdps in the class of memoryless policies"
    - "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-4 (induced by source paper "reinforcement learning of pomdp s using spectral methods"):
  with members:
    - "a pac rl algorithm for episodic pomdps"
    - "online learning and optimization of markov jump affine models"
    - "selecting near optimal approximate state representations in reinforcement learning"
    - "nonparametric estimation of multi view latent variable models"
    - "on learning parametric output hmms"
    - "pegasus a policy search method for large mdps and pomdps"
    - "tensor decompositions for learning latent variable models"
    - "a method of moments for mixture models and hidden markov models"

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)