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: "on mutual information maximization for representation learning"
  Abstract: Many recent methods for unsupervised or self-supervised representation learning train feature extractors by maximizing an estimate of the mutual information (MI) between different views of the data. This comes with several immediate problems: For example, MI is notoriously hard to estimate, and using it as an objective for representation learning may lead to highly entangled representations due to its invariance under arbitrary invertible transformations. Nevertheless, these methods have been repeatedly shown to excel in practice. In this paper we argue, and provide empirical evidence, that the success of these methods might be only loosely attributed to the properties of MI, and that they strongly depend on the inductive bias in both the choice of feature extractor architectures and the parametrization of the employed MI estimators. Finally, we establish a connection to deep metric learning and argue that this interpretation may be a plausible explanation for the success of the recently introduced methods.

Connected through hyperedge HE-1 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "large scale adversarial representation learning"
    - "classification accuracy score for conditional generative models"
    - "data efficient image recognition with contrastive predictive coding"
    - "do imagenet classifiers generalize to imagenet"
    - "remixmatch semi supervised learning with distribution alignment and augmentation anchoring"
    - "meta dataset a dataset of datasets for learning to learn from few examples"
    - "revisiting self supervised visual representation learning"

Connected through hyperedge HE-2 (induced by source paper "multi modal self supervision from generalized data transformations"):
  with members:
    - "a simple framework for contrastive learning of visual representations"
    - "audiovisual slowfast networks for video recognition"
    - "learning spatiotemporal features via video and text pair discrimination"
    - "self supervised spatio temporal representation learning for videos by predicting motion and appearance statistics"
    - "end to end learning of visual representations from uncurated instructional videos"
    - "learning video representations using contrastive bidirectional transformer"
    - "contrastive multiview coding"
    - "data efficient image recognition with contrastive predictive coding"

Connected through hyperedge HE-3 (induced by source paper "a simple framework for contrastive learning of visual representations"):
  with members:
    - "fixmatch simplifying semi supervised learning with consistency and confidence"
    - "contrastive multiview coding"
    - "momentum contrast for unsupervised visual representation learning"
    - "large scale adversarial representation learning"
    - "data efficient image recognition with contrastive predictive coding"
    - "local aggregation for unsupervised learning of visual embeddings"
    - "unsupervised data augmentation for consistency training"
    - "learning representations by maximizing mutual information across views"

Connected through hyperedge HE-4 (induced by source paper "automatic shortcut removal for self supervised representation learning"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "self supervised learning of video induced visual invariances"
    - "contrastive multiview coding"
    - "momentum contrast for unsupervised visual representation learning"
    - "data efficient image recognition with contrastive predictive coding"
    - "adversarial examples improve image recognition"
    - "learning representations by maximizing mutual information across views"
    - "revisiting self supervised visual representation learning"

Connected through hyperedge HE-5 (induced by source paper "learning robust representations via multi view information bottleneck"):
  with members:
    - "semantically tied paired cycle consistency for zero shot sketch based image retrieval"
    - "contrastive multiview coding"
    - "on variational bounds of mutual information"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "mine mutual information neural estimation"
    - "representation learning with contrastive predictive coding"
    - "deep variational canonical correlation analysis"
    - "emergence of invariance and disentanglement in deep representations"

Connected through hyperedge HE-6 (induced by source paper "dhog deep hierarchical object grouping"):
  with members:
    - "deep comprehensive correlation mining for image clustering"
    - "randaugment practical automated data augmentation with a reduced search space"
    - "randaugment practical data augmentation with no separate search"
    - "fast autoaugment"
    - "learning representations by maximizing mutual information across views"
    - "cinic 10 is not imagenet or cifar 10"
    - "deep k means jointly clustering with k means and learning representations"
    - "deep clustering for unsupervised learning of visual features"

Connected through hyperedge HE-7 (induced by source paper "conditional mutual information based contrastive loss for financial time series forecasting"):
  with members:
    - "understanding the limitations of variational mutual information estimators"
    - "contrastive multiview coding"
    - "on variational bounds of mutual information"
    - "ccmi classifier based conditional mutual information estimation"
    - "a dual stage attention based recurrent neural network for time series prediction"
    - "attention is all you need"
    - "wavenet a generative model for raw audio"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "unsupervised deep metric learning via auxiliary rotation loss"):
  with members:
    - "multi similarity loss with general pair weighting for deep metric learning"
    - "hardness aware deep metric learning"
    - "using self supervised learning can improve model robustness and uncertainty"
    - "unsupervised pre training of image features on non curated data"
    - "data efficient image recognition with contrastive predictive coding"
    - "learning representations by maximizing mutual information across views"
    - "unsupervised embedding learning via invariant and spreading instance feature"
    - "grasp2vec learning object representations from self supervised grasping"

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.LG(Machine Learning)