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 nonlinear dynamic models"
  Abstract: We present a novel approach for learning nonlinear dynamic models, which leads to a new set of tools capable of solving problems that are otherwise difficult. We provide theory showing this new approach is consistent for models with long range structure, and apply the approach to motion capture and high-dimensional video data, yielding results superior to standard alternatives.

Connected through hyperedge HE-1 (induced by source paper "contrastive estimation reveals topic posterior information to linear models"):
  with members:
    - "overparameterized neural networks can implement associative memory"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "an efficient framework for learning sentence representations"
    - "character level convolutional networks for text classification"
    - "unsupervised learning of visual representations using videos"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "learning topic models going beyond svd"

Connected through hyperedge HE-2 (induced by source paper "learning stochastic behaviour of aggregate data"):
  with members:
    - "learning deep hidden nonlinear dynamics from aggregate data"
    - "spectral normalization for generative adversarial networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "socially aware kalman neural networks for trajectory prediction"):
  with members:
    - "convolutional social pooling for vehicle trajectory prediction"
    - "sequence to sequence prediction of vehicle trajectory via lstm encoder decoder architecture"
    - "multi agent imitation learning for driving simulation"
    - "imitating driver behavior with generative adversarial networks"
    - "backprop kf learning discriminative deterministic state estimators"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-4 (induced by source paper "learning deep hidden nonlinear dynamics from aggregate data"):
  with members:
    - "spectrally normalized margin bounds for neural networks"
    - "improved training of wasserstein gans"
    - "c rnn gan continuous recurrent neural networks with adversarial training"
    - "provable bayesian inference via particle mirror descent"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "learning to filter with predictive state inference machines"):
  with members:
    - "deeply learning the messages in message passing inference"
    - "predictive state representations a new theory for modeling dynamical systems"
    - "adadelta an adaptive learning rate method"
    - "theano new features and speed improvements"
    - "stability conditions for online learnability"
    - "optimistic rates for learning with a smooth loss"
    - "predictive state temporal difference learning"
    - "a reduction of imitation learning and structured prediction to no regret online learning"

Connected through hyperedge HE-6 (induced by source paper "embed to control a locally linear latent dynamics model for control from raw images"):
  with members:
    - "draw a recurrent neural network for image generation"
    - "end to end training of deep visuomotor policies"
    - "adam a method for stochastic optimization"
    - "nice non linear independent components estimation"
    - "latent kullback leibler control for continuous state systems using probabilistic graphical models"
    - "learning to generate chairs with convolutional neural networks"
    - "transformation properties of learned visual representations"
    - "exact solutions to the nonlinear dynamics of learning in deep linear neural networks"

Connected through hyperedge HE-7 (induced by source paper "reduced rank hidden markov models"):
  with members:
    - "predictive state representations a new theory for modeling dynamical systems"
    - "closing the learning planning loop with predictive state representations"
    - "a spectral algorithm for learning hidden markov models"
    - "learning nonsingular phylogenies and hidden markov models"
    - "non negative matrix factorization with sparseness constraints"

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)