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: "repr improved training of convolutional filters"
  Abstract: A well-trained Convolutional Neural Network can easily be pruned without significant loss of performance. This is because of unnecessary overlap in the features captured by the network's filters. Innovations in network architecture such as skip/dense connections and Inception units have mitigated this problem to some extent, but these improvements come with increased computation and memory requirements at run-time. We attempt to address this problem from another angle - not by changing the network structure but by altering the training method. We show that by temporarily pruning and then restoring a subset of the model's filters, and repeating this process cyclically, overlap in the learned features is reduced, producing improved generalization. We show that the existing model-pruning criteria are not optimal for selecting filters to prune in this context and introduce inter-filter orthogonality as the ranking criteria to determine under-expressive filters. Our method is applicable both to vanilla convolutional networks and more complex modern architectures, and improves the performance across a variety of tasks, especially when applied to smaller networks.

Connected through hyperedge HE-1 (induced by source paper "pruning convolutional neural networks with self supervision"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "linear mode connectivity and the lottery ticket hypothesis"
    - "contrastive multiview coding"
    - "unsupervised pre training of image features on non curated data"
    - "data efficient image recognition with contrastive predictive coding"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "learning representations by maximizing mutual information across views"

Connected through hyperedge HE-2 (induced by source paper "filter grafting for deep neural networks"):
  with members:
    - "omni scale feature learning for person re identification"
    - "importance estimation for neural network pruning"
    - "exploring linear relationship in feature map subspace for convnets compression"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "network transplanting"
    - "scsp spectral clustering filter pruning with soft self adaption manners"
    - "network compression using correlation analysis of layer responses"

Connected through hyperedge HE-3 (induced by source paper "gradual channel pruning while training using feature relevance scores for convolutional neural networks"):
  with members:
    - "really should we pruning after model be totally trained pruning based on a small amount of training"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "discrimination aware channel pruning for deep neural networks"
    - "rethinking the value of network pruning"
    - "nisp pruning networks using neuron importance score propagation"
    - "thinet a filter level pruning method for deep neural network compression"
    - "more is less a more complicated network with less inference complexity"
    - "channel pruning for accelerating very deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "student specialization in deep relu networks with finite width and input dimension"):
  with members:
    - "luck matters understanding training dynamics of deep relu networks"
    - "measuring the intrinsic dimension of objective landscapes"
    - "small nonlinearities in activation functions create bad local minima in neural networks"
    - "the committee machine computational to statistical gaps in learning a two layers neural network"
    - "gradient descent finds global minima of deep neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "mass an accelerated stochastic method for over parametrized learning"
    - "a convergence theory for deep learning via over parameterization"

Connected through hyperedge HE-5 (induced by source paper "accelerating cnn training by sparsifying activation gradients"):
  with members:
    - "recent advances in efficient computation of deep convolutional neural networks"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "value aware quantization for training and inference of neural networks"
    - "imagenet training in minutes"
    - "learning efficient convolutional networks through network slimming"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "sparse communication for distributed gradient descent"
    - "exploring the regularity of sparse structure in convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "a generalized and robust method towards practical gaze estimation on smart phone"):
  with members:
    - "evaluation of appearance based methods and implications for gaze based applications"
    - "few shot adaptive gaze estimation"
    - "improving few shot user specific gaze adaptation via gaze redirection synthesis"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "snapshot distillation teacher student optimization in one generation"
    - "feature denoising for improving adversarial robustness"
    - "knowledge distillation in generations more tolerant teachers educate better students"
    - "soft filter pruning for accelerating deep convolutional neural networks"

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)