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: "filter response normalization layer eliminating batch dependence in the training of deep neural networks"
  Abstract: Batch Normalization (BN) is a highly successful and widely used batch dependent training method. Its use of mini-batch statistics to normalize the activations introduces dependence between samples, which can hurt the training if the mini-batch size is too small, or if the samples are correlated. Several alternatives, such as Batch Renormalization and Group Normalization (GN), have been proposed to address these issues. However, they either do not match the performance of BN for large batches, or still exhibit degradation in performance for smaller batches, or introduce artificial constraints on the model architecture. In this paper we propose the Filter Response Normalization (FRN) layer, a novel combination of a normalization and an activation function, that can be used as a drop-in replacement for other normalizations and activations. Our method operates on each activation map of each batch sample independently, eliminating the dependency on other batch samples or channels of the same sample. Our method outperforms BN and all alternatives in a variety of settings for all batch sizes. FRN layer performs $\approx 0.7-1.0\%$ better on top-1 validation accuracy than BN with large mini-batch sizes on Imagenet classification on InceptionV3 and ResnetV2-50 architectures. Further, it performs $>1\%$ better than GN on the same problem in the small mini-batch size regime. For object detection problem on COCO dataset, FRN layer outperforms all other methods by at least $0.3-0.5\%$ in all batch size regimes.

Connected through hyperedge HE-1 (induced by source paper "batch normalization biases deep residual networks towards shallow paths"):
  with members:
    - "provable benefit of orthogonal initialization in optimizing deep linear networks"
    - "freeze and chaos for dnns an ntk view of batch normalization checkerboard and boundary effects"
    - "a mean field theory of batch normalization"
    - "the effect of network width on stochastic gradient descent and generalization an empirical study"
    - "fixup initialization residual learning without normalization"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "the impact of neural network overparameterization on gradient confusion and stochastic gradient descent"
    - "self training with noisy student improves imagenet classification"

Connected through hyperedge HE-2 (induced by source paper "be like water robustness to extraneous variables via adaptive feature normalization"):
  with members:
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "two at once enhancing learning and generalization capacities via ibn net"
    - "adversarial discriminative domain adaptation"
    - "instance normalization the missing ingredient for fast stylization"
    - "end to end optimized image compression"
    - "deep transfer learning with joint adaptation networks"
    - "densely connected convolutional networks"
    - "unsupervised domain adaptation with residual transfer networks"

Connected through hyperedge HE-3 (induced by source paper "mean shift rejection training deep neural networks without minibatch statistics or normalization"):
  with members:
    - "fixup initialization residual learning without normalization"
    - "generalized batch normalization towards accelerating deep neural networks"
    - "deep residual networks and weight initialization"
    - "comparison of batch normalization and weight normalization algorithms for the large scale image classification"
    - "improved techniques for training gans"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "recurrent batch normalization"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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.LG(Machine Learning)