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: "pca initialized deep neural networks applied to document image analysis"
  Abstract: In this paper, we present a novel approach for initializing deep neural networks, i.e., by using Principal Component Analysis (PCA) to initialize neural layers. Usually, the initialization of the weights of a deep neural network is done in one of the three following ways: 1) with random values, 2) layer-wise, usually as Deep Belief Network or as auto-encoder, and 3) re-use of layers from another network (transfer learning). Therefore, typically, many training epochs are needed before meaningful weights are learned, or a rather similar dataset is required for seeding a fine-tuning of transfer learning. In this paper, we describe how to turn a PCA into an auto-encoder, by generating an encoder layer of the PCA parameters and furthermore adding a decoding layer. We analyze the initialization technique on real documents. First, we show that a PCA-based initialization is quick and leads to a very stable initialization. Furthermore, for the task of layout analysis we investigate the effectiveness of PCAbased initialization and show that it outperforms state-of-the-art random weight initialization methods.

Connected through hyperedge HE-1 (induced by source paper "trainable spectrally initializable matrix transformations in convolutional neural networks"):
  with members:
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "deepdiva a highly functional python framework for reproducible experiments"
    - "historical document image segmentation with lda initialized deep neural networks"
    - "squeeze and excitation networks"
    - "deep learning in neural networks"
    - "deep residual learning for image recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "deep scattering spectrum"

Connected through hyperedge HE-2 (induced by source paper "spectral clustering via ensemble deep autoencoder learning sc edae"):
  with members:
    - "on the convergence of adam and beyond"
    - "deep k means jointly clustering with k means and learning representations"
    - "deep subspace clustering networks"
    - "fast spectral clustering using autoencoders and landmarks"
    - "towards k means friendly spaces simultaneous deep learning and clustering"
    - "unsupervised deep embedding for clustering analysis"
    - "generalized denoising auto encoders as generative models"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-3 (induced by source paper "a low effort approach to structured cnn design using pca"):
  with members:
    - "importance estimation for neural network pruning"
    - "explainable learning implicit generative modelling during training for adversarial robustness"
    - "discrimination aware channel pruning for deep neural networks"
    - "snip single shot network pruning based on connection sensitivity"
    - "structured binary neural networks for accurate image classification and semantic segmentation"
    - "nisp pruning networks using neuron importance score propagation"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "thinet a filter level pruning method for deep neural network compression"

Connected through hyperedge HE-4 (induced by source paper "lifted neural networks"):
  with members:
    - "training neural networks without gradients a scalable admm approach"
    - "trusting svm for piecewise linear cnns"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"
    - "sketching as a tool for numerical linear algebra"
    - "distributed optimization of deeply nested systems"

Connected through hyperedge HE-5 (induced by source paper "deepdiva a highly functional python framework for reproducible experiments"):
  with members:
    - "identifying cross depicted historical motifs"
    - "historical document image segmentation with lda initialized deep neural networks"
    - "deep residual learning for image recognition"
    - "openml networked science in machine learning"
    - "learning fine grained image similarity with deep ranking"

Connected through hyperedge HE-6 (induced by source paper "cutting the error by half investigation of very deep cnn and advanced training strategies for document image classification"):
  with members:
    - "a generic method for automatic ground truth generation of camera captured documents"
    - "deep residual learning for image recognition"
    - "evaluation of deep convolutional nets for document image classification and retrieval"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"
    - "network in network"

Connected through hyperedge HE-7 (induced by source paper "historical document image segmentation with lda initialized deep neural networks"):
  with members:
    - "open evaluation tool for layout analysis of document images"
    - "what you expect is not what you get questioning reconstruction classification correlation of stacked convolutional auto encoder features"
    - "deep learning in neural networks"
    - "deep linear discriminant analysis"
    - "data dependent initializations of 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.LG(Machine Learning)