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: "learning finite state controllers for partially observable environments"
  Abstract: Reactive (memoryless) policies are sufficient in completely observable Markov decision processes (MDPs), but some kind of memory is usually necessary for optimal control of a partially observable MDP. Policies with finite memory can be represented as finite-state automata. In this paper, we extend Baird and Moore's VAPS algorithm to the problem of learning general finite-state automata. Because it performs stochastic gradient descent, this algorithm can be shown to converge to a locally optimal finite-state controller. We provide the details of the algorithm and then consider the question of under what conditions stochastic gradient descent will outperform exact gradient descent. We conclude with empirical results comparing the performance of stochastic and exact gradient descent, and showing the ability of our algorithm to extract the useful information contained in the sequence of past observations to compensate for the lack of observability at each time-step.

Connected through hyperedge HE-1 (induced by source paper "counterexample guided strategy improvement for pomdps using recurrent neural networks"):
  with members:
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "a storm is coming a modern probabilistic model checker"
    - "deep recurrent q learning for partially observable mdps"
    - "on the computational complexity of stochastic controller optimization in pomdps"

Connected through hyperedge HE-2 (induced by source paper "online cyber attack detection in smart grid a reinforcement learning approach"):
  with members:
    - "real time detection of hybrid and stealthy cyber attacks in smart grid"
    - "learning policies with external memory"

Connected through hyperedge HE-3 (induced by source paper "reinforcement learning in pomdps with memoryless options and option observation initiation sets"):
  with members:
    - "a deep hierarchical approach to lifelong learning in minecraft"
    - "adam a method for stochastic optimization"
    - "closing the learning planning loop with predictive state representations"
    - "learning policies with external memory"

Connected through hyperedge HE-4 (induced by source paper "contextual mdps for pac reinforcement learning with rich observations"):
  with members:
    - "reinforcement learning of pomdp s using spectral methods"
    - "online learning in mdps with side information"
    - "predictive state representations a new theory for modeling dynamical systems"
    - "contextual bandit learning with predictable rewards"

Connected through hyperedge HE-5 (induced by source paper "pac reinforcement learning with rich observations"):
  with members:
    - "reinforcement learning of pomdps using spectral methods"
    - "contextual bandit learning with predictable rewards"

Connected through hyperedge HE-6 (induced by source paper "deep learning in neural networks"):
  with members:
    - "going deeper with convolutions"
    - "event driven contrastive divergence for spiking neuromorphic systems"
    - "a clockwork rnn"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "sequence to sequence learning with neural networks"
    - "multi column deep neural networks for offline handwritten chinese character classification"
    - "dropout improves recurrent neural networks for handwriting recognition"
    - "efficient visual coding from retina to v2"

Connected through hyperedge HE-7 (induced by source paper "on learning to think algorithmic information theory for novel combinations of reinforcement learning controllers and recurrent neural world models"):
  with members:
    - "deep learning in neural networks"
    - "going deeper with convolutions"
    - "neural turing machines"
    - "deep speech scaling up end to end speech recognition"
    - "a clockwork rnn"
    - "show and tell a neural image caption generator"
    - "sequence to sequence learning with neural networks"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "learning deep neural network policies with continuous memory states"):
  with members:
    - "embed to control a locally linear latent dynamics model for control from raw images"
    - "learning contact rich manipulation skills with guided policy search"
    - "end to end training of deep visuomotor policies"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "sequence to sequence learning with neural networks"
    - "hierarchical pomdp controller optimization by likelihood maximization"
    - "on the difficulty of training recurrent neural networks"
    - "a reduction of imitation learning and structured prediction to no regret online learning"

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)