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: "self supervised feature learning by learning to spot artifacts"
  Abstract: We introduce a novel self-supervised learning method based on adversarial training. Our objective is to train a discriminator network to distinguish real images from images with synthetic artifacts, and then to extract features from its intermediate layers that can be transferred to other data domains and tasks. To generate images with artifacts, we pre-train a high-capacity autoencoder and then we use a damage and repair strategy: First, we freeze the autoencoder and damage the output of the encoder by randomly dropping its entries. Second, we augment the decoder with a repair network, and train it in an adversarial manner against the discriminator. The repair network helps generate more realistic images by inpainting the dropped feature entries. To make the discriminator focus on the artifacts, we also make it predict what entries in the feature were dropped. We demonstrate experimentally that features learned by creating and spotting artifacts achieve state of the art performance in several benchmarks.

Connected through hyperedge HE-1 (induced by source paper "unsupervised multi task feature learning on point clouds"):
  with members:
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "3d point capsule networks"
    - "clustering with deep learning taxonomy and new methods"
    - "multiresolution tree networks for 3d point cloud processing"
    - "deep clustering for unsupervised learning of visual features"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "ppf foldnet unsupervised learning of rotation invariant 3d local descriptors"
    - "splatnet sparse lattice networks for point cloud processing"

Connected through hyperedge HE-2 (induced by source paper "when does self supervision improve few shot learning"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "surprising effectiveness of few image unsupervised feature learning"
    - "meta learning with differentiable convex optimization"
    - "few shot learning with localization in realistic settings"
    - "boosting few shot visual learning with self supervision"
    - "large scale adversarial representation learning"
    - "unsupervised pre training of image features on non curated data"
    - "domain generalization by solving jigsaw puzzles"

Connected through hyperedge HE-3 (induced by source paper "a critical analysis of self supervision or what we can learn from a single image"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "singan learning a generative model from a single natural image"
    - "boosting self supervised learning via knowledge transfer"
    - "deep clustering for unsupervised learning of visual features"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "cross pixel optical flow similarity for self supervised learning"
    - "unsupervised representation learning by predicting image rotations"
    - "exploring the limits of weakly supervised pretraining"

Connected through hyperedge HE-4 (induced by source paper "self labelling via simultaneous clustering and representation learning"):
  with members:
    - "aet vs aed unsupervised representation learning by auto encoding transformations rather than data"
    - "unsupervised deep learning by neighbourhood discovery"
    - "surprising effectiveness of few image unsupervised feature learning"
    - "data efficient image recognition with contrastive predictive coding"
    - "a critical analysis of self supervision or what we can learn from a single image"
    - "local aggregation for unsupervised learning of visual embeddings"
    - "learning representations by maximizing mutual information across views"
    - "revisiting self supervised visual representation learning"

Connected through hyperedge HE-5 (induced by source paper "unsupervised pre training of image features on non curated data"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "aet vs aed unsupervised representation learning by auto encoding transformations rather than data"
    - "revisiting self supervised visual representation learning"
    - "rethinking imagenet pre training"
    - "learning image representations by completing damaged jigsaw puzzles"
    - "boosting self supervised learning via knowledge transfer"
    - "deep clustering for unsupervised learning of visual features"
    - "cross pixel optical flow similarity for self supervised learning"

Connected through hyperedge HE-6 (induced by source paper "surprising effectiveness of few image unsupervised feature learning"):
  with members:
    - "boosting self supervised learning via knowledge transfer"
    - "deep clustering for unsupervised learning of visual features"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "unsupervised representation learning by predicting image rotations"
    - "transitive invariance for self supervised visual representation learning"
    - "improvements to context based self supervised learning"
    - "deep image prior"
    - "colorization as a proxy task for visual understanding"

Connected through hyperedge HE-7 (induced by source paper "multi task self supervised learning for human activity detection"):
  with members:
    - "measuring the intrinsic dimension of objective landscapes"
    - "deep learning for sensor based activity recognition a survey"
    - "unsupervised representation learning by predicting image rotations"
    - "universal language model fine tuning for text classification"
    - "protecting sensory data against sensitive inferences"
    - "cooperative learning of audio and video models from self supervised synchronization"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "audio visual scene analysis with self supervised multisensory features"

Connected through hyperedge HE-8 (induced by source paper "local aggregation for unsupervised learning of visual embeddings"):
  with members:
    - "contrastive multiview coding"
    - "data efficient image recognition with contrastive predictive coding"
    - "learning representations by maximizing mutual information across views"
    - "deep clustering for unsupervised learning of visual features"
    - "unsupervised representation learning by predicting image rotations"
    - "improving generalization via scalable neighborhood component analysis"
    - "representation learning with contrastive predictive coding"
    - "transitive invariance for self supervised visual representation 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)