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: "learning invariant representations with local transformations"
  Abstract: Learning invariant representations is an important problem in machine learning and pattern recognition. In this paper, we present a novel framework of transformation-invariant feature learning by incorporating linear transformations into the feature learning algorithms. For example, we present the transformation-invariant restricted Boltzmann machine that compactly represents data by its weights and their transformations, which achieves invariance of the feature representation via probabilistic max pooling. In addition, we show that our transformation-invariant feature learning framework can also be extended to other unsupervised learning methods, such as autoencoders or sparse coding. We evaluate our method on several image classification benchmark datasets, such as MNIST variations, CIFAR-10, and STL-10, and show competitive or superior classification performance when compared to the state-of-the-art. Furthermore, our method achieves state-of-the-art performance on phone classification tasks with the TIMIT dataset, which demonstrates wide applicability of our proposed algorithms to other domains.

Connected through hyperedge HE-1 (induced by source paper "graphter unsupervised learning of graph transformation equivariant representations via auto encoding node wise transformations"):
  with members:
    - "aet vs aed unsupervised representation learning by auto encoding transformations rather than data"
    - "avt unsupervised learning of transformation equivariant representations by autoencoding variational transformations"
    - "learning generalized transformation equivariant representations via autoencoding transformations"
    - "l2g auto encoder understanding point clouds by local to global reconstruction with hierarchical self attention"
    - "relation shape convolutional neural network for point cloud analysis"
    - "multi angle point cloud vae unsupervised feature learning for 3d point clouds from multiple angles by joint self reconstruction and half to half prediction"
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"

Connected through hyperedge HE-2 (induced by source paper "transform invariant convolutional neural networks for image classification and search"):
  with members:
    - "ti pooling transformation invariant pooling for feature learning in convolutional neural networks"
    - "interactive inter layer activeness propagation"
    - "learning attributes equals multi source domain generalization"
    - "jointly modeling embedding and translation to bridge video and language"
    - "contextual action recognition with r cnn"
    - "spatial transformer networks"
    - "deep residual learning for image recognition"
    - "deepfont identify your font from an image"

Connected through hyperedge HE-3 (induced by source paper "patch reordering a novel way to achieve rotation and translation invariance in convolutional neural networks"):
  with members:
    - "contextual action recognition with r cnn"
    - "spatial transformer networks"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "locally scale invariant convolutional neural networks"
    - "caffe convolutional architecture for fast feature embedding"
    - "understanding image representations by measuring their equivariance and equivalence"

Connected through hyperedge HE-4 (induced by source paper "affine disentangled gan for interpretable and robust av perception"):
  with members:
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "inferencing based on unsupervised learning of disentangled representations"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"
    - "synthesizing robust adversarial examples"
    - "towards evaluating the robustness of neural networks"
    - "adversarial examples in the physical world"
    - "practical black box attacks against deep learning systems using adversarial examples"

Connected through hyperedge HE-5 (induced by source paper "unsupervised learning from videos using temporal coherency deep networks"):
  with members:
    - "transitive invariance for self supervised visual representation learning"
    - "rank pooling for action recognition"
    - "the thumos challenge on action recognition for videos in the wild"
    - "pn net conjoined triple deep network for learning local image descriptors"
    - "unsupervised learning of visual representations using videos"
    - "a robust and efficient video representation for action recognition"
    - "learning local image descriptors with deep siamese and triplet convolutional networks by minimising global loss functions"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-6 (induced by source paper "invariant information clustering for unsupervised image classification and segmentation"):
  with members:
    - "deep clustering for unsupervised learning of visual features"
    - "mine mutual information neural estimation"
    - "deeppermnet visual permutation learning"
    - "scaling the scattering transform deep hybrid networks"
    - "learning to segment every thing"
    - "unsupervised representation learning by sorting sequences"
    - "improved regularization of convolutional neural networks with cutout"
    - "deep unsupervised similarity learning using partially ordered sets"

Connected through hyperedge HE-7 (induced by source paper "non parametric transformation networks"):
  with members:
    - "max margin invariant features from transformed unlabeled data"
    - "dynamic routing between capsules"
    - "deep rotation equivariant network"
    - "squeeze and excitation networks"
    - "dual path networks"
    - "graph based isometry invariant representation learning"
    - "harmonic networks deep translation and rotation equivariance"
    - "learning to extract motion from videos in convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "evolving deep convolutional neural networks by variable length particle swarm optimization for image classification"):
  with members:
    - "a particle swarm optimization based flexible convolutional autoencoder for image classification"
    - "evolving unsupervised deep neural networks for learning meaningful representations"
    - "large scale evolution of image classifiers"
    - "evolving deep convolutional neural networks for image classification"
    - "a genetic programming approach to designing convolutional neural network architectures"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

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)