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: "graph based isometry invariant representation learning"
  Abstract: Learning transformation invariant representations of visual data is an important problem in computer vision. Deep convolutional networks have demonstrated remarkable results for image and video classification tasks. However, they have achieved only limited success in the classification of images that undergo geometric transformations. In this work we present a novel Transformation Invariant Graph-based Network (TIGraNet), which learns graph-based features that are inherently invariant to isometric transformations such as rotation and translation of input images. In particular, images are represented as signals on graphs, which permits to replace classical convolution and pooling layers in deep networks with graph spectral convolution and dynamic graph pooling layers that together contribute to invariance to isometric transformations. Our experiments show high performance on rotated and translated images from the test set compared to classical architectures that are very sensitive to transformations in the data. The inherent invariance properties of our framework provide key advantages, such as increased resiliency to data variability and sustained performance with limited training sets.

Connected through hyperedge HE-1 (induced by source paper "spectral graph attention network"):
  with members:
    - "power up robust graph convolutional network against evasion attacks based on graph powering"
    - "stabilizing gans with octave convolutions"
    - "a restricted black box adversarial framework towards attacking graph embedding models"
    - "lanczosnet multi scale deep graph convolutional networks"
    - "measuring and relieving the over smoothing problem for graph neural networks from the topological view"
    - "graph neural networks with convolutional arma filters"
    - "dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems"
    - "semi supervised graph classification a hierarchical graph perspective"

Connected through hyperedge HE-2 (induced by source paper "image classification with hierarchical multigraph networks"):
  with members:
    - "graph u nets"
    - "spectral multigraph networks for discovering and fusing relationships in molecules"
    - "iterative visual reasoning beyond convolutions"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "hierarchical graph representation learning with differentiable pooling"
    - "relational inductive biases deep learning and graph networks"
    - "how powerful are graph neural networks"
    - "splinecnn fast geometric deep learning with continuous b spline kernels"

Connected through hyperedge HE-3 (induced by source paper "gift learning transformation invariant dense visual descriptors via group cnns"):
  with members:
    - "equivariant multi view networks"
    - "equivariant transformer networks"
    - "an improved learning framework for covariant local feature detection"
    - "local descriptors optimized for average precision"
    - "large scale evaluation of local image feature detectors on homography datasets"
    - "superpoint self supervised interest point detection and description"
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"
    - "instance normalization the missing ingredient for fast stylization"

Connected through hyperedge HE-4 (induced by source paper "temporal transformer networks joint learning of invariant and discriminative time warping"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "can recurrent neural networks warp time"
    - "learning to exploit invariances in clinical time series data using sequence transformer networks"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "dynamic routing between capsules"
    - "learning invariant riemannian geometric representations using deep nets"
    - "a new representation of skeleton sequences for 3d action recognition"
    - "view adaptive recurrent neural networks for high performance human action recognition from skeleton data"

Connected through hyperedge HE-5 (induced by source paper "on the choice of graph neural network architectures"):
  with members:
    - "graph neural networks exponentially lose expressive power for node classification"
    - "simplifying graph convolutional networks"
    - "fast graph representation learning with pytorch geometric"
    - "structured agents for physical construction"
    - "a comprehensive survey on graph neural networks"
    - "predict then propagate graph neural networks meet personalized pagerank"
    - "dual primal graph convolutional networks"
    - "pitfalls of graph neural network evaluation"

Connected through hyperedge HE-6 (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"
    - "learning to extract motion from videos in convolutional neural networks"
    - "aggregated residual transformations for deep neural networks"
    - "warped convolutions efficient invariance to spatial transformations"
    - "xception deep learning with depthwise separable convolutions"

Connected through hyperedge HE-7 (induced by source paper "ipool information based pooling in hierarchical graph neural networks"):
  with members:
    - "graph u nets"
    - "hierarchical graph representation learning with differentiable pooling"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "dynamic graph cnn for learning on point clouds"
    - "how powerful are graph neural networks"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "semi supervised classification with graph convolutional networks"
    - "learning convolutional neural networks for graphs"

Connected through hyperedge HE-8 (induced by source paper "dane domain adaptive network embedding"):
  with members:
    - "tag2vec learning tag representations in tag networks"
    - "sepne bringing separability to network embedding"
    - "learning structural node embeddings via diffusion wavelets"
    - "domain adaptation with adversarial training and graph embeddings"
    - "adversarial discriminative domain adaptation"
    - "node2vec scalable feature learning for networks"
    - "multi task domain adaptation for sequence tagging"
    - "revisiting semi supervised learning with graph embeddings"

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)