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: "deeppermnet visual permutation learning"
  Abstract: We present a principled approach to uncover the structure of visual data by solving a novel deep learning task coined visual permutation learning. The goal of this task is to find the permutation that recovers the structure of data from shuffled versions of it. In the case of natural images, this task boils down to recovering the original image from patches shuffled by an unknown permutation matrix. Unfortunately, permutation matrices are discrete, thereby posing difficulties for gradient-based methods. To this end, we resort to a continuous approximation of these matrices using doubly-stochastic matrices which we generate from standard CNN predictions using Sinkhorn iterations. Unrolling these iterations in a Sinkhorn network layer, we propose DeepPermNet, an end-to-end CNN model for this task. The utility of DeepPermNet is demonstrated on two challenging computer vision problems, namely, (i) relative attributes learning and (ii) self-supervised representation learning. Our results show state-of-the-art performance on the Public Figures and OSR benchmarks for (i) and on the classification and segmentation tasks on the PASCAL VOC dataset for (ii).

Connected through hyperedge HE-1 (induced by source paper "instance separation emerges from inpainting"):
  with members:
    - "monet unsupervised scene decomposition and representation"
    - "learning correspondence from the cycle consistency of time"
    - "learning pyramid context encoder network for high quality image inpainting"
    - "selfie self supervised pretraining for image embedding"
    - "structureflow image inpainting via structure aware appearance flow"
    - "unsupervised object segmentation by redrawing"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "noise2void learning denoising from single noisy images"

Connected through hyperedge HE-2 (induced by source paper "optimal feature transport for cross view image geo localization"):
  with members:
    - "lending orientation to neural networks for cross view geo localization"
    - "feature selection for unsupervised domain adaptation using optimal transport"
    - "localizing and orienting street views using overhead imagery"
    - "semantic image based geolocation given a map"
    - "predicting ground level scene layout from aerial imagery"
    - "semantic cross view matching"
    - "optimal transport for domain adaptation"
    - "spatial transformer networks"

Connected through hyperedge HE-3 (induced by source paper "improved structural discovery and representation learning of multi agent data"):
  with members:
    - "google research football a novel reinforcement learning environment"
    - "exploiting temporal context for 3d human pose estimation in the wild"
    - "learning 3d human dynamics from video"
    - "generative multi agent behavioral cloning"
    - "exploiting temporal information for 3d human pose estimation"
    - "fine grained retrieval of sports plays using tree based alignment of trajectories"
    - "neural message passing for quantum chemistry"
    - "coordinated multi agent imitation learning"

Connected through hyperedge HE-4 (induced by source paper "sinkhorn autoencoders"):
  with members:
    - "sliced wasserstein autoencoder an embarrassingly simple generative model"
    - "implicit reparameterization gradients"
    - "lipschitz properties for deep convolutional networks"
    - "fixing a broken elbo"
    - "representation learning by learning to count"
    - "vae with a vampprior"
    - "deep variational information bottleneck"
    - "tsallis regularized optimal transport and ecological inference"

Connected through hyperedge HE-5 (induced by source paper "learning functions over sets via permutation adversarial networks"):
  with members:
    - "learning representations of sets through optimized permutations"
    - "janossy pooling learning deep permutation invariant functions for variable size inputs"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "streaming pca matching matrix bernstein and near optimal finite sample guarantees for oja s algorithm"
    - "semi supervised classification with graph convolutional networks"
    - "deepsetnet predicting sets with deep neural networks"
    - "diffusion convolutional neural networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "a novel hybrid scheme using genetic algorithms and deep learning for the reconstruction of portuguese tile panels"):
  with members:
    - "a generalized genetic algorithm based solver for very large jigsaw puzzles of complex types"
    - "unsupervised learning of visual representations by solving jigsaw puzzles"
    - "dnn buddies a deep neural network based estimation metric for the jigsaw puzzle problem"
    - "unsupervised visual representation learning by context prediction"
    - "facenet a unified embedding for face recognition and clustering"
    - "adam a method for stochastic optimization"
    - "genetic algorithm based solver for very large multiple jigsaw puzzles of unknown dimensions and piece orientation"
    - "a genetic algorithm based solver for very large jigsaw puzzles"

Connected through hyperedge HE-7 (induced by source paper "auto labelling of markers in optical motion capture by permutation learning"):
  with members:
    - "deep perm set net learn to predict sets with unknown permutation and cardinality using deep neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "iterative reorganization with weak spatial constraints solving arbitrary jigsaw puzzles for unsupervised representation learning"):
  with members:
    - "improving spatiotemporal self supervision by deep reinforcement learning"
    - "boosting self supervised learning via knowledge transfer"
    - "deep clustering for unsupervised learning of visual features"
    - "deep co training for semi supervised image recognition"
    - "unsupervised representation learning by predicting image rotations"
    - "bridging the gap between 2d and 3d organ segmentation with volumetric fusion net"
    - "recurrent saliency transformation network incorporating multi stage visual cues for small organ segmentation"
    - "improvements to context based self supervised learning"

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)