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 learning of visual features through embedding images into text topic spaces"
  Abstract: End-to-end training from scratch of current deep architectures for new computer vision problems would require Imagenet-scale datasets, and this is not always possible. In this paper we present a method that is able to take advantage of freely available multi-modal content to train computer vision algorithms without human supervision. We put forward the idea of performing self-supervised learning of visual features by mining a large scale corpus of multi-modal (text and image) documents. We show that discriminative visual features can be learnt efficiently by training a CNN to predict the semantic context in which a particular image is more probable to appear as an illustration. For this we leverage the hidden semantic structures discovered in the text corpus with a well-known topic modeling technique. Our experiments demonstrate state of the art performance in image classification, object detection, and multi-modal retrieval compared to recent self-supervised or natural-supervised approaches.

Connected through hyperedge HE-1 (induced by source paper "variational hetero encoder randomized gans for joint image text modeling"):
  with members:
    - "object driven text to image synthesis via adversarial training"
    - "introvae introspective variational autoencoders for photographic image synthesis"
    - "photographic text to image synthesis with a hierarchically nested adversarial network"
    - "text adaptive generative adversarial networks manipulating images with natural language"
    - "link the head to the beak zero shot learning from noisy text description at part precision"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "recent advances in zero shot recognition"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"

Connected through hyperedge HE-2 (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-3 (induced by source paper "scene text visual question answering"):
  with members:
    - "towards vqa models that can read"
    - "icdar 2019 competition on scene text visual question answering"
    - "single shot scene text retrieval"
    - "fots fast oriented text spotting with a unified network"
    - "an end to end textspotter with explicit alignment and attention"
    - "tallyqa answering complex counting questions"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "textboxes a single shot oriented scene text detector"

Connected through hyperedge HE-4 (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-5 (induced by source paper "variational hetero encoder randomized generative adversarial networks for joint image text modeling"):
  with members:
    - "photographic text to image synthesis with a hierarchically nested adversarial network"
    - "recent advances in zero shot recognition"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"
    - "topically driven neural language model"
    - "flow gan combining maximum likelihood and adversarial learning in generative models"
    - "less is more zero shot learning from online textual documents with noise suppression"
    - "improved techniques for training gans"

Connected through hyperedge HE-6 (induced by source paper "self supervised learning from web data for multimodal retrieval"):
  with members:
    - "learning from barcelona instagram data what locals and tourists post about its neighbourhoods"
    - "exploring the limits of weakly supervised pretraining"
    - "texttopicnet self supervised learning of visual features through embedding images on semantic text spaces"
    - "webvision challenge visual learning and understanding with web data"
    - "webvision database visual learning and understanding from web data"
    - "enriching word vectors with subword information"
    - "synthetic data for text localisation in natural images"
    - "understanding neural networks through deep visualization"

Connected through hyperedge HE-7 (induced by source paper "self supervised visual representations for cross modal retrieval"):
  with members:
    - "texttopicnet self supervised learning of visual features through embedding images on semantic text spaces"
    - "transitive invariance for self supervised visual representation learning"
    - "ambient sound provides supervision for visual learning"
    - "a comprehensive survey on cross modal retrieval"
    - "context encoders feature learning by inpainting"
    - "unsupervised visual representation learning by context prediction"
    - "unsupervised learning of visual representations using videos"
    - "data dependent initializations of convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "mining on manifolds metric learning without labels"):
  with members:
    - "nearest neighbour radial basis function solvers for deep neural networks"
    - "polysemous codes"
    - "parametric manifold learning via sparse multidimensional scaling"
    - "sampling matters in deep embedding learning"
    - "transitive invariance for self supervised visual representation learning"
    - "iterative manifold embedding layer learned by incomplete data for large scale image retrieval"
    - "multi task self supervised visual learning"
    - "no fuss distance metric learning using proxies"

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)