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: "using self supervised learning can improve model robustness and uncertainty"
  Abstract: Self-supervision provides effective representations for downstream tasks without requiring labels. However, existing approaches lag behind fully supervised training and are often not thought beneficial beyond obviating the need for annotations. We find that self-supervision can benefit robustness in a variety of ways, including robustness to adversarial examples, label corruption, and common input corruptions. Additionally, self-supervision greatly benefits out-of-distribution detection on difficult, near-distribution outliers, so much so that it exceeds the performance of fully supervised methods. These results demonstrate the promise of self-supervision for improving robustness and uncertainty estimation and establish these tasks as new axes of evaluation for future self-supervised learning research.

Connected through hyperedge HE-1 (induced by source paper "learning representations by predicting bags of visual words"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "cutmix regularization strategy to train strong classifiers with localizable features"
    - "s4l self supervised semi supervised learning"
    - "when does self supervision improve few shot learning"
    - "contrastive multiview coding"
    - "boosting few shot visual learning with self supervision"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "momentum contrast for unsupervised visual representation learning"

Connected through hyperedge HE-2 (induced by source paper "a comprehensive study on the prediction reliability of graph neural networks for virtual screening"):
  with members:
    - "bayesian deep learning and a probabilistic perspective of generalization"
    - "when does label smoothing help"
    - "data efficient image recognition with contrastive predictive coding"
    - "self attention graph pooling"
    - "pre training graph neural networks"
    - "dropconnect is effective in modeling uncertainty of bayesian deep networks"
    - "bayesian semi supervised learning for uncertainty calibrated prediction of molecular properties and active learning"
    - "using pre training can improve model robustness and uncertainty"

Connected through hyperedge HE-3 (induced by source paper "compact surjective encoding autoencoder for unsupervised novelty detection"):
  with members:
    - "deep semi supervised anomaly detection"
    - "one class convolutional neural network"
    - "ocgan one class novelty detection using gans with constrained latent representations"
    - "adversarially learned one class classifier for novelty detection"
    - "deep anomaly detection using geometric transformations"
    - "latent space autoregression for novelty detection"
    - "spectral normalization for generative adversarial networks"
    - "deep anomaly detection with outlier exposure"

Connected through hyperedge HE-4 (induced by source paper "a multi view perspective of self supervised learning"):
  with members:
    - "rethinking data augmentation self supervision and self distillation"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "unsupervised domain adaptation through self supervision"
    - "revisiting self supervised visual representation learning"
    - "self supervised gans via auxiliary rotation loss"
    - "deep clustering for unsupervised learning of visual features"
    - "unsupervised representation learning by predicting image rotations"
    - "unsupervised learning of disentangled representations from video"

Connected through hyperedge HE-5 (induced by source paper "drocc deep robust one class classification"):
  with members:
    - "deep semi supervised anomaly detection"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "anomaly detection with multiple hypotheses predictions"
    - "deep anomaly detection using geometric transformations"
    - "deep anomaly detection with outlier exposure"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"
    - "lstm based encoder decoder for multi sensor anomaly detection"
    - "unsupervised learning of video representations using lstms"

Connected through hyperedge HE-6 (induced by source paper "unsupervised domain adaptation through self supervision"):
  with members:
    - "what is local optimality in nonconvex nonconcave minimax optimization"
    - "domain generalization by solving jigsaw puzzles"
    - "boosting supervision with self supervision for few shot learning"
    - "stabilizing gan training with multiple random projections"
    - "deep visual domain adaptation a survey"
    - "unsupervised representation learning by predicting image rotations"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "representation learning with contrastive predictive coding"

Connected through hyperedge HE-7 (induced by source paper "natural adversarial examples"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "towards neural networks that provably know when they don t know"
    - "verified uncertainty calibration"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "detecting semantic anomalies"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"
    - "on evaluating adversarial robustness"

Connected through hyperedge HE-8 (induced by source paper "towards shape biased unsupervised representation learning for domain generalization"):
  with members:
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "domain generalization by solving jigsaw puzzles"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "unsupervised domain adaptation using feature whitening and consensus loss"
    - "learning image representations by completing damaged jigsaw puzzles"
    - "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"

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)