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: "data dependent initializations of convolutional neural networks"
  Abstract: Convolutional Neural Networks spread through computer vision like a wildfire, impacting almost all visual tasks imaginable. Despite this, few researchers dare to train their models from scratch. Most work builds on one of a handful of ImageNet pre-trained models, and fine-tunes or adapts these for specific tasks. This is in large part due to the difficulty of properly initializing these networks from scratch. A small miscalibration of the initial weights leads to vanishing or exploding gradients, as well as poor convergence properties. In this work we present a fast and simple data-dependent initialization procedure, that sets the weights of a network such that all units in the network train at roughly the same rate, avoiding vanishing or exploding gradients. Our initialization matches the current state-of-the-art unsupervised or self-supervised pre-training methods on standard computer vision tasks, such as image classification and object detection, while being roughly three orders of magnitude faster. When combined with pre-training methods, our initialization significantly outperforms prior work, narrowing the gap between supervised and unsupervised pre-training.

Connected through hyperedge HE-1 (induced by source paper "scaling and benchmarking self supervised visual representation learning"):
  with members:
    - "revisiting self supervised visual representation learning"
    - "mid level visual representations improve generalization and sample efficiency for learning active tasks"
    - "boosting self supervised learning via knowledge transfer"
    - "deep clustering for unsupervised learning of visual features"
    - "gibson env real world perception for embodied agents"
    - "mid level visual representations improve generalization and sample efficiency for learning visuomotor policies"
    - "learning to separate object sounds by watching unlabeled video"
    - "unsupervised representation learning by predicting image rotations"

Connected through hyperedge HE-2 (induced by source paper "contrastive multiview coding"):
  with members:
    - "learning robust and multilingual speech representations"
    - "self supervised learning of video induced visual invariances"
    - "end to end learning of visual representations from uncurated instructional videos"
    - "momentum contrast for unsupervised visual representation learning"
    - "large scale adversarial representation learning"
    - "randaugment practical data augmentation with no separate search"
    - "a survey of multi view representation learning"
    - "data efficient image recognition with contrastive predictive coding"

Connected through hyperedge HE-3 (induced by source paper "a critical analysis of self supervision or what we can learn from a single image"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "singan learning a generative model from a single natural image"
    - "boosting self supervised learning via knowledge transfer"
    - "deep clustering for unsupervised learning of visual features"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "cross pixel optical flow similarity for self supervised learning"
    - "unsupervised representation learning by predicting image rotations"
    - "exploring the limits of weakly supervised pretraining"

Connected through hyperedge HE-4 (induced by source paper "the impact of neural network overparameterization on gradient confusion and stochastic gradient descent"):
  with members:
    - "generalization in deep networks the role of distance from initialization"
    - "a mean field theory of batch normalization"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "fixup initialization residual learning without normalization"
    - "stiffness a new perspective on generalization in neural networks"
    - "a modern take on the bias variance tradeoff in neural networks"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "learning and generalization in overparameterized neural networks going beyond two layers"

Connected through hyperedge HE-5 (induced by source paper "distinit learning video representations without a single labeled video"):
  with members:
    - "boosting self supervised learning via knowledge transfer"
    - "taskonomy disentangling task transfer learning"
    - "a better baseline for ava"
    - "video action transformer network"
    - "unsupervised representation learning by predicting image rotations"
    - "spatio temporal channel correlation networks for action classification"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-6 (induced by source paper "surprising effectiveness of few image unsupervised feature learning"):
  with members:
    - "boosting self supervised learning via knowledge transfer"
    - "deep clustering for unsupervised learning of visual features"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "unsupervised representation learning by predicting image rotations"
    - "self supervised feature learning by learning to spot artifacts"
    - "transitive invariance for self supervised visual representation learning"
    - "improvements to context based self supervised learning"
    - "deep image prior"

Connected through hyperedge HE-7 (induced by source paper "local aggregation for unsupervised learning of visual embeddings"):
  with members:
    - "contrastive multiview coding"
    - "data efficient image recognition with contrastive predictive coding"
    - "learning representations by maximizing mutual information across views"
    - "deep clustering for unsupervised learning of visual features"
    - "unsupervised representation learning by predicting image rotations"
    - "improving generalization via scalable neighborhood component analysis"
    - "representation learning with contrastive predictive coding"
    - "self supervised feature learning by learning to spot artifacts"

Connected through hyperedge HE-8 (induced by source paper "self supervised representation learning via neighborhood relational encoding"):
  with members:
    - "variational autoencoder with truncated mixture of gaussians for functional connectivity analysis"
    - "deep clustering for unsupervised learning of visual features"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "adversarially learned one class classifier for novelty detection"
    - "avid adversarial visual irregularity detection"
    - "unsupervised representation learning by predicting image rotations"
    - "self supervised feature learning by learning to spot artifacts"
    - "unsupervised person re identification by deep learning tracklet association"

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)