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: "unsupervised learning of invariant representations in hierarchical architectures"
  Abstract: Representations that are invariant to translation, scale and other transformations, can considerably reduce the sample complexity of learning, allowing recognition of new object classes from very few examples|a hallmark of human recognition. Empirical estimates of one-dimensional projections of the distribution induced by a group of ane transformations are proven to represent a unique and invariant signature associated with an image. We show how projections yielding invariant signatures for future images can be learned automatically, and updated continuously, during unsupervised visual experience. A module performing ltering and pooling, like simple and complex cells as proposed by Hubel and Wiesel, can compute such estimates. Under this view, a pooling stage estimates a one-dimensional probability distribution. Invariance from observations through a restricted window is equivalent to a sparsity property w.r.t. to a transformation, which yields templates that are a) Gabor for optimal simultaneous invariance to translation and scale or b) very specic for complex, class-dependent transformations such as rotation in depth of faces. Hierarchical architectures consisting of this basic Hubel-Wiesel module inherit its properties of invariance, stability, and discriminability while capturing the compositional organization of the visual world in terms of wholes and parts, and are invariant to complex transformations that may only be locally ane. The theory applies to several existing deep learning convolutional architectures for image and speech recognition. It also suggests that the main computational goal of the ventral stream of visual cortex is to provide a hierarchical representation of new objects/images which is invariant to transformations, stable, and discriminative for recognition|and that this representation may be continuously learned in an unsupervised way during development and natural visual experience.

Connected through hyperedge HE-1 (induced by source paper "learning non parametric invariances from data with permanent random connectomes"):
  with members:
    - "exploring randomly wired neural networks for image recognition"
    - "autoaugment learning augmentation policies from data"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "deep rotation equivariant network"
    - "graph based isometry invariant representation learning"
    - "learning to extract motion from videos in convolutional neural networks"
    - "aggregated residual transformations for deep neural networks"
    - "warped convolutions efficient invariance to spatial transformations"

Connected through hyperedge HE-2 (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-3 (induced by source paper "quantifying translation invariance in convolutional neural networks"):
  with members:
    - "spatial transformer networks"
    - "adam a method for stochastic optimization"
    - "understanding image representations by measuring their equivariance and equivalence"
    - "invariant scattering convolution networks"

Connected through hyperedge HE-4 (induced by source paper "learning data manifolds with a cutting plane method"):
  with members:
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-5 (induced by source paper "variational inference of disentangled latent concepts from unlabeled observations"):
  with members:
    - "unsupervised learning of disentangled representations from video"
    - "towards deeper understanding of variational autoencoding models"
    - "local group invariant representations via orbit embeddings"
    - "understanding visual concepts with continuation learning"
    - "a survey of inductive biases for factorial representation learning"
    - "adversarial feature learning"
    - "deep feature consistent variational autoencoder"
    - "weakly supervised disentangling with recurrent transformations for 3d view synthesis"

Connected through hyperedge HE-6 (induced by source paper "compression of deep neural networks for image instance retrieval"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "nested invariance pooling and rbm hashing for image instance retrieval"
    - "group invariant deep representations for image instance retrieval"
    - "bridging the gaps between residual learning recurrent neural networks and visual cortex"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"
    - "deep residual learning for image recognition"
    - "particular object retrieval with integral max pooling of cnn activations"

Connected through hyperedge HE-7 (induced by source paper "on the effect of pooling on the geometry of representations"):
  with members:
    - "exploiting cyclic symmetry in convolutional neural networks"
    - "local group invariant representations via orbit embeddings"
    - "group equivariant convolutional networks"
    - "learning with group invariant features a kernel perspective"
    - "understanding deep features with computer generated imagery"
    - "a mathematical theory of deep convolutional neural networks for feature extraction"
    - "on invariance and selectivity in representation learning"
    - "understanding image representations by measuring their equivariance and equivalence"

Connected through hyperedge HE-8 (induced by source paper "how convnets model non linear transformations"):
  with members:
    - "global optimality in tensor factorization deep learning and beyond"
    - "on the expressive power of deep learning a tensor analysis"
    - "on the expressive efficiency of sum product networks"
    - "why does deep learning work a perspective from group theory"
    - "learning stable group invariant representations with convolutional 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)