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: "deep predictive coding networks"
  Abstract: The quality of data representation in deep learning methods is directly related to the prior model imposed on the representations; however, generally used fixed priors are not capable of adjusting to the context in the data. To address this issue, we propose deep predictive coding networks, a hierarchical generative model that empirically alters priors on the latent representations in a dynamic and context-sensitive manner. This model captures the temporal dependencies in time-varying signals and uses top-down information to modulate the representation in lower layers. The centerpiece of our model is a novel procedure to infer sparse states of a dynamic model which is used for feature extraction. We also extend this feature extraction block to introduce a pooling function that captures locally invariant representations. When applied on a natural video data, we show that our method is able to learn high-level visual features. We also demonstrate the role of the top-down connections by showing the robustness of the proposed model to structured noise.

Connected through hyperedge HE-1 (induced by source paper "prednet and predictive coding a critical review"):
  with members:
    - "inception inspired lstm for next frame video prediction"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "predrnn towards a resolution of the deep in time dilemma in spatiotemporal predictive learning"
    - "deep predictive coding network for object recognition"
    - "deep predictive coding network with local recurrent processing for object recognition"
    - "afa prednet the action modulation within predictive coding"
    - "representation learning with contrastive predictive coding"
    - "the something something video database for learning and evaluating visual common sense"

Connected through hyperedge HE-2 (induced by source paper "dynamic variational autoencoders for visual process modeling"):
  with members:
    - "alignment distances on systems of bags"
    - "dyan a dynamical atoms based network for video prediction"
    - "two stream convolutional networks for dynamic texture synthesis"
    - "visual dynamics probabilistic future frame synthesis via cross convolutional networks"
    - "generating videos with scene dynamics"
    - "embed to control a locally linear latent dynamics model for control from raw images"

Connected through hyperedge HE-3 (induced by source paper "lifelong neural predictive coding sparsity yields less forgetting when learning cumulatively"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"
    - "continual learning of recurrent neural networks by locally aligning distributed representations"
    - "biologically motivated algorithms for propagating local target representations"
    - "conducting credit assignment by aligning local representations"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "progressive neural networks"
    - "learning to adapt by minimizing discrepancy"

Connected through hyperedge HE-4 (induced by source paper "ctnn corticothalamic inspired neural network"):
  with members:
    - "one time learning in a biologically inspired salience affected artificial neural network sann"
    - "deep predictive coding network for object recognition"
    - "deep predictive coding networks for video prediction and unsupervised learning"
    - "multi column deep neural networks for image classification"
    - "fast inference in sparse coding algorithms with applications to object recognition"

Connected through hyperedge HE-5 (induced by source paper "continual learning of recurrent neural networks by locally aligning distributed representations"):
  with members:
    - "a neural temporal model for human motion prediction"
    - "sparse attentive backtracking long range credit assignment in recurrent networks"
    - "approximating real time recurrent learning with random kronecker factors"
    - "biologically motivated algorithms for propagating local target representations"
    - "conducting credit assignment by aligning local representations"
    - "lstm a search space odyssey"
    - "learning to adapt by minimizing discrepancy"
    - "learning simpler language models with the differential state framework"

Connected through hyperedge HE-6 (induced by source paper "deep predictive coding network for object recognition"):
  with members:
    - "learning transferable architectures for scalable image recognition"
    - "cortexnet a generic network family for robust visual temporal representations"
    - "squeeze and excitation networks"
    - "identity mappings in deep residual networks"
    - "densely connected convolutional networks"
    - "deep predictive coding networks for video prediction and unsupervised learning"
    - "wide residual networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "prediction under uncertainty with error encoding networks"):
  with members:
    - "decomposing motion and content for natural video sequence prediction"
    - "unsupervised learning for physical interaction through video prediction"
    - "unrolled generative adversarial networks"
    - "improved techniques for training gans"
    - "image to image translation with conditional adversarial networks"
    - "deep predictive coding networks for video prediction and unsupervised learning"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "anticipating the future by watching unlabeled video"

Connected through hyperedge HE-8 (induced by source paper "linearizing visual processes with convolutional variational autoencoders"):
  with members:
    - "alignment distances on systems of bags"
    - "dyan a dynamical atoms based network for video prediction"
    - "two stream convolutional networks for dynamic texture synthesis"
    - "dynamical textures modeling via joint video dictionary learning"
    - "visual dynamics probabilistic future frame synthesis via cross convolutional networks"
    - "group equivariant convolutional networks"
    - "generating videos with scene dynamics"
    - "procedural generation of videos to train deep action recognition networks"

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)