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: "a novel predictive coding inspired variational rnn model for online prediction and recognition"
  Abstract: This study introduces PV-RNN, a novel variational RNN inspired by the predictive-coding ideas. The model learns to extract the probabilistic structures hidden in fluctuating temporal patterns by dynamically changing the stochasticity of its latent states. Its architecture attempts to address two major concerns of variational Bayes RNNs: how can latent variables learn meaningful representations and how can the inference model transfer future observations to the latent variables. PV-RNN does both by introducing adaptive vectors mirroring the training data, whose values can then be adapted differently during evaluation. Moreover, prediction errors during backpropagation, rather than external inputs during the forward computation, are used to convey information to the network about the external data. For testing, we introduce error regression for predicting unseen sequences as inspired by predictive coding that leverages those mechanisms. The model introduces a weighting parameter, the meta-prior, to balance the optimization pressure placed on two terms of a lower bound on the marginal likelihood of the sequential data. We test the model on two datasets with probabilistic structures and show that with high values of the meta-prior the network develops deterministic chaos through which the data's randomness is imitated. For low values, the model behaves as a random process. The network performs best on intermediate values, and is able to capture the latent probabilistic structure with good generalization. Analyzing the meta-prior's impact on the network allows to precisely study the theoretical value and practical benefits of incorporating stochastic dynamics in our model. We demonstrate better prediction performance on a robot imitation task with our model using error regression compared to a standard variational Bayes model lacking such a procedure.

Connected through hyperedge HE-1 (induced by source paper "investigation of multimodal and agential interactions in human robot imitation based on frameworks of predictive coding and active inference"):
  with members:
    - "active inference body perception and action for humanoid robots"
    - "cognitive and motor compliance in intentional human robot interaction"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "a recurrent latent variable model for sequential data"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "minor constraint disturbances for deep semi supervised learning"):
  with members:
    - "semi supervised semantic segmentation with high and low level consistency"
    - "generalized matrix means for semi supervised learning with multilayer graphs"
    - "sg fcn a motion and memory based deep learning model for video saliency detection"
    - "stacking based deep neural network deep analytic network for pattern classification"
    - "restricted boltzmann machines with gaussian visible units guided by pairwise constraints"

Connected through hyperedge HE-3 (induced by source paper "self organization of action hierarchy and compositionality by reinforcement learning with recurrent neural networks"):
  with members:
    - "model based reinforcement learning for atari"
    - "learning abstract options"
    - "bayesian model agnostic meta learning"
    - "one big net for everything"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "probabilistic model agnostic meta learning"
    - "recurrent world models facilitate policy evolution"
    - "learning to embed probabilistic structures between deterministic chaos and random process in a variational bayes predictive coding rnn"

Connected through hyperedge HE-4 (induced by source paper "variational recurrent models for solving partially observable control tasks"):
  with members:
    - "self organization of action hierarchy and compositionality by reinforcement learning with recurrent neural networks"
    - "stochastic latent actor critic deep reinforcement learning with a latent variable model"
    - "model based reinforcement learning for atari"
    - "learning latent dynamics for planning from pixels"
    - "soft actor critic algorithms and applications"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "deep variational reinforcement learning for pomdps"
    - "recurrent world models facilitate policy evolution"

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)