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: "aet vs aed unsupervised representation learning by auto encoding transformations rather than data"
  Abstract: The success of deep neural networks often relies on a large amount of labeled examples, which can be difficult to obtain in many real scenarios. To address this challenge, unsupervised methods are strongly preferred for training neural networks without using any labeled data. In this paper, we present a novel paradigm of unsupervised representation learning by Auto-Encoding Transformation (AET) in contrast to the conventional Auto-Encoding Data (AED) approach. Given a randomly sampled transformation, AET seeks to predict it merely from the encoded features as accurately as possible at the output end. The idea is the following: as long as the unsupervised features successfully encode the essential information about the visual structures of original and transformed images, the transformation can be well predicted. We will show that this AET paradigm allows us to instantiate a large variety of transformations, from parameterized, to non-parameterized and GAN-induced ones. Our experiments show that AET greatly improves over existing unsupervised approaches, setting new state-of-the-art performances being greatly closer to the upper bounds by their fully supervised counterparts on CIFAR-10, ImageNet and Places datasets.

Connected through hyperedge HE-1 (induced by source paper "self supervised learning of pretext invariant representations"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "s4l self supervised semi supervised learning"
    - "learning video representations using contrastive bidirectional transformer"
    - "contrastive multiview coding"
    - "momentum contrast for unsupervised visual representation learning"
    - "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-2 (induced by source paper "self labelling via simultaneous clustering and representation learning"):
  with members:
    - "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"
    - "self supervised learning of pretext invariant representations"

Connected through hyperedge HE-3 (induced by source paper "unsupervised pre training of image features on non curated data"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "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"
    - "unsupervised representation learning by predicting image rotations"

Connected through hyperedge HE-4 (induced by source paper "exploring self supervised regularization for supervised and semi supervised learning"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "bridging theory and algorithm for domain adaptation"
    - "label propagation for deep semi supervised learning"
    - "mixmatch a holistic approach to semi supervised learning"
    - "revisiting self supervised visual representation learning"
    - "high fidelity image generation with fewer labels"
    - "there are many consistent explanations of unlabeled data why you should average"

Connected through hyperedge HE-5 (induced by source paper "towards a hypothesis on visual transformation based self supervision"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "self supervised dialogue learning"
    - "contrastive multiview coding"
    - "self supervised exploration via disagreement"
    - "using self supervised learning can improve model robustness and uncertainty"
    - "the visual task adaptation benchmark"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "selfie self supervised pretraining for image embedding"

Connected through hyperedge HE-6 (induced by source paper "graphter unsupervised learning of graph transformation equivariant representations via auto encoding node wise transformations"):
  with members:
    - "avt unsupervised learning of transformation equivariant representations by autoencoding variational transformations"
    - "learning generalized transformation equivariant representations via autoencoding 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-7 (induced by source paper "rethinking data augmentation self supervision and self distillation"):
  with members:
    - "s4l self supervised semi supervised learning"
    - "learning imbalanced datasets with label distribution aware margin loss"
    - "meta learning with differentiable convex optimization"
    - "using self supervised learning can improve model robustness and uncertainty"
    - "class balanced loss based on effective number of samples"
    - "self supervised gans via auxiliary rotation loss"
    - "meta learning with latent embedding optimization"
    - "knowledge distillation by on the fly native ensemble"

Connected through hyperedge HE-8 (induced by source paper "adversarial fisher vectors for unsupervised representation learning"):
  with members:
    - "implicit generation and generalization in energy based models"
    - "task2vec task embedding for meta learning"
    - "spectral normalization for generative adversarial networks"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "unsupervised representation learning by predicting image rotations"
    - "which training methods for gans do actually converge"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "stabilizing training of generative adversarial networks through regularization"

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)