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 network for object recognition"
  Abstract: Inspired by predictive coding in neuroscience, we designed a bi-directional and recurrent neural net, namely deep predictive coding networks (PCN). It uses convolutional layers in both feedforward and feedback networks, and recurrent connections within each layer. Feedback connections from a higher layer carry the prediction of its lower-layer representation; feedforward connections carry the prediction errors to its higher-layer. Given image input, PCN runs recursive cycles of bottom-up and top-down computation to update its internal representations to reduce the difference between bottom-up input and top-down prediction at every layer. After multiple cycles of recursive updating, the representation is used for image classification. In training, the classification error backpropagates across layers and in time. With benchmark data (CIFAR-10/100, SVHN, and MNIST), PCN was found to always outperform its feedforward-only counterpart: a model without any mechanism for recurrent dynamics, and its performance tended to improve given more cycles of computation over time. In short, PCN reuses a single architecture to recursively run bottom-up and top-down process, enabling an increasingly longer cascade of non-linear transformation. For image classification, PCN refines its representation over time towards more accurate and definitive recognition.

Connected through hyperedge HE-1 (induced by source paper "differential dynamic programming neural optimizer"):
  with members:
    - "symplectic ode net learning hamiltonian dynamics with control"
    - "painless stochastic gradient interpolation line search and convergence rates"
    - "feedback network for image super resolution"
    - "understanding and improving transformer from a multi particle dynamic system point of view"
    - "neural sde stabilizing neural ode networks with stochastic noise"
    - "deep learning theory review an optimal control and dynamical systems perspective"
    - "negative eigenvalues of the hessian in deep neural networks"
    - "hamiltonian neural networks"

Connected through hyperedge HE-2 (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 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"
    - "unsupervised learning for physical interaction through video prediction"

Connected through hyperedge HE-3 (induced by source paper "bringing giant neural networks down to earth with unlabeled data"):
  with members:
    - "importance estimation for neural network pruning"
    - "from hashing to cnns training binaryweight networks via hashing"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "discrimination aware channel pruning for deep neural networks"
    - "recent advances in efficient computation of deep convolutional neural networks"
    - "exploring the limits of weakly supervised pretraining"
    - "rethinking the value of network pruning"
    - "dynamic channel pruning feature boosting and suppression"

Connected through hyperedge HE-4 (induced by source paper "controversial stimuli pitting neural networks against each other as models of human recognition"):
  with members:
    - "mnist c a robustness benchmark for computer vision"
    - "conditional generative models are not robust"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "detecting and diagnosing adversarial images with class conditional capsule reconstructions"
    - "humans can decipher adversarial images"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "towards the first adversarially robust neural network model on mnist"
    - "darccc detecting adversaries by reconstruction from class conditional capsules"

Connected through hyperedge HE-5 (induced by source paper "disentangling neural mechanisms for perceptual grouping"):
  with members:
    - "one shot segmentation in clutter"
    - "can recurrent neural networks warp time"
    - "sample efficient image segmentation through recurrence"
    - "robust neural circuit reconstruction from serial electron microscopy with convolutional recurrent networks"
    - "superhuman accuracy on the snemi3d connectomics challenge"
    - "dynamic routing between capsules"
    - "understanding deep learning requires rethinking generalization"
    - "deep predictive coding networks for video prediction and unsupervised learning"

Connected through hyperedge HE-6 (induced by source paper "video action classification using prednet"):
  with members:
    - "encoding longer term contextual multi modal information in a predictive coding model"
    - "predrnn towards a resolution of the deep in time dilemma in spatiotemporal predictive learning"
    - "deep predictive coding network with local recurrent processing for object recognition"
    - "afa prednet the action modulation within predictive coding"
    - "learning to generate long term future via hierarchical prediction"
    - "the something something video database for learning and evaluating visual common sense"
    - "unsupervised learning for physical interaction through video prediction"
    - "deep predictive coding networks for video prediction and unsupervised learning"

Connected through hyperedge HE-7 (induced by source paper "a neurally inspired hierarchical prediction network for spatiotemporal sequence learning and prediction"):
  with members:
    - "hierarchical long term video prediction without supervision"
    - "predrnn towards a resolution of the deep in time dilemma in spatiotemporal predictive learning"
    - "stochastic video generation with a learned prior"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "learning to generate long term future via hierarchical prediction"
    - "unsupervised learning for physical interaction through video prediction"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "deep predictive coding networks for video prediction and unsupervised learning"

Connected through hyperedge HE-8 (induced by source paper "effect of top down connections in hierarchical sparse coding"):
  with members:
    - "multilayer convolutional sparse modeling pursuit and dictionary learning"
    - "deep predictive coding network with local recurrent processing for object recognition"
    - "deep predictive coding networks for video prediction and unsupervised learning"
    - "winner take all autoencoders"
    - "k sparse autoencoders"
    - "convolutional matching pursuit and dictionary training"

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.CV(Computer Vision and Pattern Recognition)