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 generalized transformation equivariant representations via autoencoding transformations"
  Abstract: Learning Transformation Equivariant Representations (TERs) seeks to capture the intrinsic visual structures of images through the representations that equivary to the applied transformations. It assumes that a transformation should be decoded from expressive representations of images before and after transformations. It greatly expands the scope of {\em translation} equivariance pinpointing the success of the Convolutional Neural Networks (CNNs) to develop a generic class of {\em transformation} equivariant representations. Unlike group equivariant convolutions that are limited to discrete transformations or linear transformation equivariance, we present a more flexible and tractable AutoEncoding Transformation (AET) model that can handle various types of transformations. Both deterministic AET and probabilistic Autoencoding Variational Transformations (AVT) models are presented. While the former trains transformation equivariant representations by directly reconstructing applied transformations, the latter is trained by maximizing the joint mutual information between the representations and the transformations. It leads to the Generalized TERs (GTERs) that could equivary against transformations in a more general manner by enabling them to capture more complex patterns of transformed visual structures beyond the linear TERs of a transformation group. We will further show that the presented approach can be extended to (semi-)supervised models by jointly maximizing the mutual information in the learned representations about the input labels and transformations. Experiment results following the standard evaluation protocols demonstrate the superior performances of the proposed models to the existing state-of-the-art unsupervised and (semi-)supervised approaches in literature.

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"
    - "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"
    - "a graph cnn for 3d point cloud classification"

Connected through hyperedge HE-2 (induced by source paper "avt unsupervised learning of transformation equivariant representations by autoencoding variational transformations"):
  with members:
    - "aet vs aed unsupervised representation learning by auto encoding transformations rather than data"
    - "deep clustering for unsupervised learning of visual features"
    - "group equivariant capsule networks"
    - "unsupervised representation learning by predicting image rotations"
    - "intertwiners between induced representations with applications to the theory of equivariant neural networks"
    - "loss sensitive generative adversarial networks on lipschitz densities"
    - "dynamic routing between capsules"
    - "scaling the scattering transform deep hybrid networks"

Connected through hyperedge HE-3 (induced by source paper "aetv2 autoencoding transformations for self supervised representation learning by minimizing geodesic distances in lie groups"):
  with members:
    - "avt unsupervised learning of transformation equivariant representations by autoencoding variational transformations"
    - "revisiting self supervised visual representation learning"
    - "deep clustering for unsupervised learning of visual features"
    - "unsupervised representation learning by predicting image rotations"
    - "representation learning by learning to count"
    - "adversarial feature learning"
    - "unsupervised learning of visual representations by solving jigsaw puzzles"
    - "split brain autoencoders unsupervised learning by cross channel prediction"

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)