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 bag of words equivalent recurrent neural network for action recognition"
  Abstract: The traditional bag-of-words approach has found a wide range of applications in computer vision. The standard pipeline consists of a generation of a visual vocabulary, a quantization of the features into histograms of visual words, and a classification step for which usually a support vector machine in combination with a non-linear kernel is used. Given large amounts of data, however, the model suffers from a lack of discriminative power. This applies particularly for action recognition, where the vast amount of video features needs to be subsampled for unsupervised visual vocabulary generation. Moreover, the kernel computation can be very expensive on large datasets. In this work, we propose a recurrent neural network that is equivalent to the traditional bag-of-words approach but enables for the application of discriminative training. The model further allows to incorporate the kernel computation into the neural network directly, solving the complexity issue and allowing to represent the complete classification system within a single network. We evaluate our method on four recent action recognition benchmarks and show that the conventional model as well as sparse coding methods are outperformed.

Connected through hyperedge HE-1 (induced by source paper "are good local minima wide in sparse recovery"):
  with members:
    - "on learning optimized reaction diffusion processes for effective image restoration"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-2 (induced by source paper "learnable pooling with context gating for video classification"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "aggregating frame level features for large scale video classification"
    - "the monkeytyping solution to the youtube 8m video understanding challenge"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "squeeze and excitation networks"
    - "temporal modeling approaches for large scale youtube 8m video understanding"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "language modeling with gated convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "recurrent residual learning for action recognition"):
  with members:
    - "rank pooling for action recognition"
    - "temporal segment networks towards good practices for deep action recognition"
    - "spatiotemporal residual networks for video action recognition"
    - "deep residual learning for image recognition"
    - "towards good practices for very deep two stream convnets"
    - "action recognition with trajectory pooled deep convolutional descriptors"
    - "adam a method for stochastic optimization"
    - "long term recurrent convolutional networks for visual recognition and description"

Connected through hyperedge HE-4 (induced by source paper "classifying unordered feature sets with convolutional deep averaging networks"):
  with members:
    - "permutation equivariant neural networks applied to dynamics prediction"
    - "learning distributed representations of sentences from unlabelled data"
    - "group equivariant convolutional networks"
    - "theano a python framework for fast computation of mathematical expressions"
    - "deep residual learning for image recognition"
    - "unsupervised deep haar scattering on graphs"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-5 (induced by source paper "recasting residual based local descriptors as convolutional neural networks an application to image forgery detection"):
  with members:
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "handcrafted local features are convolutional neural networks"
    - "adam a method for stochastic optimization"
    - "visualizing and understanding convolutional networks"
    - "representation learning a review and new perspectives"

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)