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 with local recurrent processing for object recognition"
  Abstract: Inspired by "predictive coding" - a theory in neuroscience, we develop a bi-directional and dynamical neural network with local recurrent processing, namely predictive coding network (PCN). Unlike any feedforward-only convolutional neural network, PCN includes both feedback connections, which carry top-down predictions, and feedforward connections, which carry bottom-up errors of prediction. Feedback and feedforward connections enable adjacent layers to interact locally and recurrently to refine representations towards minimization of layer-wise prediction errors. When unfolded over time, the recurrent processing gives rise to an increasingly deeper hierarchy of non-linear transformation, allowing a shallow network to dynamically extend itself into an arbitrarily deep network. We train and test PCN for image classification with SVHN, CIFAR and ImageNet datasets. Despite notably fewer layers and parameters, PCN achieves competitive performance compared to classical and state-of-the-art models. Further analysis shows that the internal representations in PCN converge over time and yield increasingly better accuracy in object recognition. Errors of top-down prediction also map visual saliency or bottom-up attention. This work takes us one step closer to bridging human and machine intelligence in vision.

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"
    - "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-2 (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 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-3 (induced by source paper "effect of top down connections in hierarchical sparse coding"):
  with members:
    - "deep predictive coding network for object recognition"
    - "multilayer convolutional sparse modeling pursuit and dictionary learning"
    - "deep predictive coding networks for video prediction and unsupervised learning"
    - "winner take all autoencoders"
    - "k sparse autoencoders"
    - "convolutional matching pursuit and dictionary training"

Connected through hyperedge HE-4 (induced by source paper "recurrent connectivity aids recognition of partly occluded objects"):
  with members:
    - "bridging the gaps between residual learning recurrent neural networks and visual cortex"
    - "benchmarking in manipulation research using the yale cmu berkeley object and model set"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "making predictive coding networks generative"):
  with members:
    - "deep predictive coding networks for video prediction and unsupervised learning"
    - "equilibrium propagation bridging the gap between energy based models and backpropagation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "robust neural circuit reconstruction from serial electron microscopy with convolutional recurrent networks"):
  with members:
    - "bi directional cascade network for perceptual edge detection"
    - "deep predictive coding network for object recognition"
    - "recurrent residual convolutional neural network based on u net r2u net for medical image segmentation"
    - "learning long range spatial dependencies with horizontal gated recurrent units"
    - "do cifar 10 classifiers generalize to cifar 10"
    - "can recurrent neural networks warp time"
    - "learning what and where to attend"
    - "the elephant in the room"

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)