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: "gradually updated neural networks for large scale image recognition"
  Abstract: Depth is one of the keys that make neural networks succeed in the task of large-scale image recognition. The state-of-the-art network architectures usually increase the depths by cascading convolutional layers or building blocks. In this paper, we present an alternative method to increase the depth. Our method is by introducing computation orderings to the channels within convolutional layers or blocks, based on which we gradually compute the outputs in a channel-wise manner. The added orderings not only increase the depths and the learning capacities of the networks without any additional computation costs, but also eliminate the overlap singularities so that the networks are able to converge faster and perform better. Experiments show that the networks based on our method achieve the state-of-the-art performances on CIFAR and ImageNet datasets.

Connected through hyperedge HE-1 (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"
    - "deep predictive coding network for object recognition"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "neural rejuvenation improving deep network training by enhancing computational resource utilization"):
  with members:
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "deep clustering for unsupervised learning of visual features"
    - "stacked cross attention for image text matching"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "multi scale spatially asymmetric recalibration for image classification"
    - "deep co training for semi supervised image recognition"
    - "knowledge distillation in generations more tolerant teachers educate better students"
    - "efficient neural architecture search via parameter sharing"

Connected through hyperedge HE-3 (induced by source paper "deep co training for semi supervised image recognition"):
  with members:
    - "multi scale spatially asymmetric recalibration for image classification"
    - "good semi supervised learning that requires a bad gan"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "few shot image recognition by predicting parameters from activations"
    - "sort second order response transform for visual recognition"
    - "introspective classification with convolutional nets"
    - "single shot object detection with enriched semantics"
    - "adversarial examples for semantic segmentation and object detection"

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)