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: "invariant information clustering for unsupervised image classification and segmentation"
  Abstract: We present a novel clustering objective that learns a neural network classifier from scratch, given only unlabelled data samples. The model discovers clusters that accurately match semantic classes, achieving state-of-the-art results in eight unsupervised clustering benchmarks spanning image classification and segmentation. These include STL10, an unsupervised variant of ImageNet, and CIFAR10, where we significantly beat the accuracy of our closest competitors by 6.6 and 9.5 absolute percentage points respectively. The method is not specialised to computer vision and operates on any paired dataset samples; in our experiments we use random transforms to obtain a pair from each image. The trained network directly outputs semantic labels, rather than high dimensional representations that need external processing to be usable for semantic clustering. The objective is simply to maximise mutual information between the class assignments of each pair. It is easy to implement and rigorously grounded in information theory, meaning we effortlessly avoid degenerate solutions that other clustering methods are susceptible to. In addition to the fully unsupervised mode, we also test two semi-supervised settings. The first achieves 88.8% accuracy on STL10 classification, setting a new global state-of-the-art over all existing methods (whether supervised, semi-supervised or unsupervised). The second shows robustness to 90% reductions in label coverage, of relevance to applications that wish to make use of small amounts of labels. github.com/xu-ji/IIC

Connected through hyperedge HE-1 (induced by source paper "a comprehensive approach to unsupervised embedding learning based on and algorithm"):
  with members:
    - "unsupervised deep learning by neighbourhood discovery"
    - "unsupervised embedding learning via invariant and spreading instance feature"
    - "deep clustering for unsupervised learning of visual features"
    - "unsupervised representation learning by predicting image rotations"
    - "deep unsupervised clustering using mixture of autoencoders"
    - "representation learning by learning to count"
    - "temporal ensembling for semi supervised learning"
    - "split brain autoencoders unsupervised learning by cross channel prediction"

Connected through hyperedge HE-2 (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-3 (induced by source paper "learning representations by maximizing mutual information across views"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "contrastive multiview coding"
    - "data efficient image recognition with contrastive predictive coding"
    - "on variational bounds of mutual information"
    - "mixmatch a holistic approach to semi supervised learning"
    - "revisiting self supervised visual representation learning"
    - "rethinking imagenet pre training"
    - "noise contrastive estimation and negative sampling for conditional models consistency and statistical efficiency"

Connected through hyperedge HE-4 (induced by source paper "learning deep representations by mutual information for person re identification"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "mine mutual information neural estimation"
    - "representation learning with contrastive predictive coding"
    - "random erasing data augmentation"
    - "camera style adaptation for person re identification"
    - "image image domain adaptation with preserved self similarity and domain dissimilarity for person re identification"
    - "re ranking person re identification with k reciprocal encoding"

Connected through hyperedge HE-5 (induced by source paper "deep semi supervised anomaly detection"):
  with members:
    - "deep anomaly detection with deviation networks"
    - "using self supervised learning can improve model robustness and uncertainty"
    - "deep learning for anomaly detection a survey"
    - "unmasking clever hans predictors and assessing what machines really learn"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "an overview of deep learning based methods for unsupervised and semi supervised anomaly detection in videos"
    - "deep anomaly detection using geometric transformations"

Connected through hyperedge HE-6 (induced by source paper "unsupervised learning of object keypoints for perception and control"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "monet unsupervised scene decomposition and representation"
    - "model based reinforcement learning for atari"
    - "depth from videos in the wild unsupervised monocular depth learning from unknown cameras"
    - "multi object representation learning with iterative variational inference"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "deepmind control suite"
    - "unsupervised learning of object landmarks through conditional image generation"

Connected through hyperedge HE-7 (induced by source paper "adversarial deep embedded clustering on a better trade off between feature randomness and feature drift"):
  with members:
    - "deep clustering with a dynamic autoencoder from reconstruction towards centroids construction"
    - "efficient parameter free clustering using first neighbor relations"
    - "deep clustering with a dynamic autoencoder"
    - "understanding and improving interpolation in autoencoders via an adversarial regularizer"
    - "deep clustering for unsupervised learning of visual features"
    - "deep discriminative latent space for clustering"
    - "deep continuous clustering"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-8 (induced by source paper "enaet self trained ensemble autoencoding transformations for semi supervised learning"):
  with members:
    - "small data challenges in big data era a survey of recent progress on unsupervised and semi supervised methods"
    - "cutmix regularization strategy to train strong classifiers with localizable features"
    - "population based augmentation efficient learning of augmentation policy schedules"
    - "fast autoaugment"
    - "mixmatch a holistic approach to semi supervised learning"
    - "there are many consistent explanations of unlabeled data why you should average"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "autoaugment learning augmentation policies from data"

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)