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: "fast inference in sparse coding algorithms with applications to object recognition"
  Abstract: Adaptive sparse coding methods learn a possibly overcomplete set of basis functions, such that natural image patches can be reconstructed by linearly combining a small subset of these bases. The applicability of these methods to visual object recognition tasks has been limited because of the prohibitive cost of the optimization algorithms required to compute the sparse representation. In this work we propose a simple and efficient algorithm to learn basis functions. After training, this model also provides a fast and smooth approximator to the optimal representation, achieving even better accuracy than exact sparse coding algorithms on visual object recognition tasks.

Connected through hyperedge HE-1 (induced by source paper "an introduction to variational autoencoders"):
  with members:
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "isolating sources of disentanglement in variational autoencoders"
    - "a hybrid convolutional variational autoencoder for text generation"
    - "improved variational autoencoders for text modeling using dilated convolutions"
    - "bayesian recurrent neural networks"
    - "flow gan combining maximum likelihood and adversarial learning in generative models"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "voice conversion from unaligned corpora using variational autoencoding wasserstein generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "large memory layers with product keys"):
  with members:
    - "a jamming transition from under to over parametrization affects generalization in deep learning"
    - "transformer xl attentive language models beyond a fixed length context"
    - "cross lingual language model pretraining"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "exploring the limits of weakly supervised pretraining"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adaptive input representations for neural language modeling"

Connected through hyperedge HE-3 (induced by source paper "towards understanding residual and dilated dense neural networks via convolutional sparse coding"):
  with members:
    - "on first order meta learning algorithms"
    - "densely connected convolutional networks for speech recognition"
    - "memory efficient implementation of densenets"
    - "meta sgd learning to learn quickly for few shot learning"
    - "working locally thinking globally theoretical guarantees for convolutional sparse coding"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "tensorflow a system for large scale machine learning"

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"
    - "deep predictive coding networks"
    - "multi column deep neural networks for image classification"

Connected through hyperedge HE-5 (induced by source paper "what do we understand about convolutional networks"):
  with members:
    - "network dissection quantifying interpretability of deep visual representations"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "oriented response networks"
    - "active convolution learning the shape of convolution for image classification"
    - "deformable convolutional networks"
    - "learning texture manifolds with the periodic spatial gan"
    - "a spatiotemporal oriented energy network for dynamic texture recognition"
    - "learning to learn by gradient descent by gradient descent"

Connected through hyperedge HE-6 (induced by source paper "conducting credit assignment by aligning local representations"):
  with members:
    - "learning to adapt by minimizing discrepancy"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "decoupled neural interfaces using synthetic gradients"
    - "piecewise latent variables for neural variational text processing"
    - "a modular theory of feature learning"
    - "all you need is a good init"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-7 (induced by source paper "biologically motivated algorithms for propagating local target representations"):
  with members:
    - "multi layered gradient boosting decision trees"
    - "conducting credit assignment by aligning local representations"
    - "learning to adapt by minimizing discrepancy"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "decoupled neural interfaces using synthetic gradients"
    - "towards biologically plausible deep learning"
    - "online semi supervised learning with deep hybrid boltzmann machines and denoising autoencoders"
    - "how important is weight symmetry in backpropagation"

Connected through hyperedge HE-8 (induced by source paper "msplit lbi realizing feature selection and dense estimation simultaneously in few shot and zero shot learning"):
  with members:
    - "prototypical networks for few shot learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "zero shot learning posed as a missing data problem"
    - "learning a deep embedding model for zero shot learning"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "semi supervised vocabulary informed learning"
    - "transductive multi view zero shot learning"
    - "deep residual learning for image recognition"

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)