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: "experimental results reinforcement learning of pomdps using spectral methods"
  Abstract: We propose a new reinforcement learning algorithm for partially observable Markov decision processes (POMDP) based on spectral decomposition methods. While spectral methods have been previously employed for consistent learning of (passive) latent variable models such as hidden Markov models, POMDPs are more challenging since the learner interacts with the environment and possibly changes the future observations in the process. We devise a learning algorithm running through epochs, in each epoch we employ spectral techniques to learn the POMDP parameters from a trajectory generated by a fixed policy. At the end of the epoch, an optimization oracle returns the optimal memoryless planning policy which maximizes the expected reward based on the estimated POMDP model. We prove an order-optimal regret bound with respect to the optimal memoryless policy and efficient scaling with respect to the dimensionality of observation and action spaces.

Connected through hyperedge HE-1 (induced by source paper "deep reinforcement learning with modulated hebbian plus q network architecture"):
  with members:
    - "episodic memory deep q networks"
    - "learn what not to learn action elimination with deep reinforcement learning"
    - "deep variational reinforcement learning for pomdps"
    - "asynchronous methods for deep reinforcement learning"
    - "safe and efficient off policy reinforcement learning"
    - "learning to navigate in complex environments"
    - "prioritized experience replay"
    - "dueling network architectures for deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "trust region policy optimization of pomdps"):
  with members:
    - "signsgd compressed optimisation for non convex problems"
    - "combating deep reinforcement learning s sisyphean curse with intrinsic fear"
    - "proximal policy optimization algorithms"
    - "reinforcement learning of pomdp s using spectral methods"
    - "reinforcement learning of pomdps using spectral methods"
    - "continuous control with deep reinforcement learning"
    - "trust region policy optimization"
    - "infinite horizon policy gradient estimation"

Connected through hyperedge HE-3 (induced by source paper "reinforcement learning in rich observation mdps using spectral methods"):
  with members:
    - "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"
    - "contextual mdps for pac reinforcement learning with rich observations"
    - "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"

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)