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: "online learning with abstention"
  Abstract: We present an extensive study of the key problem of online learning where algorithms are allowed to abstain from making predictions. In the adversarial setting, we show how existing online algorithms and guarantees can be adapted to this problem. In the stochastic setting, we first point out a bias problem that limits the straightforward extension of algorithms such as UCB-N to time-varying feedback graphs, as needed in this context. Next, we give a new algorithm, UCB-GT, that exploits historical data and is adapted to time-varying feedback graphs. We show that this algorithm benefits from more favorable regret guarantees than a possible, but limited, extension of UCB-N. We further report the results of a series of experiments demonstrating that UCB-GT largely outperforms that extension of UCB-N, as well as more standard baselines.

Connected through hyperedge HE-1 (induced by source paper "fast rates for online prediction with abstention"):
  with members:
    - "fast classification rates without standard margin assumptions"
    - "combining adversarial guarantees and stochastic fast rates in online learning"
    - "the extended littlestone s dimension for learning with mistakes and abstentions"
    - "fast rates in statistical and online learning"
    - "the sample complexity of learning linear predictors with the squared loss"
    - "follow the leader if you can hedge if you must"

Connected through hyperedge HE-2 (induced by source paper "selective brain damage measuring the disparate impact of model pruning"):
  with members:
    - "natural adversarial examples"
    - "a benchmark for interpretability methods in deep neural networks"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "the state of sparsity in deep neural networks"
    - "faster gaze prediction with dense networks and fisher pruning"
    - "scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science"
    - "efficient neural audio synthesis"
    - "metric learning for novelty and anomaly detection"

Connected through hyperedge HE-3 (induced by source paper "unsupervised out of distribution detection with batch normalization"):
  with members:
    - "neural processes"
    - "are generative deep models for novelty detection truly better"
    - "unsupervised anomaly detection via variational auto encoder for seasonal kpis in web applications"
    - "out of distribution detection using an ensemble of self supervised leave out classifiers"
    - "group anomaly detection using deep generative models"
    - "deep anomaly detection with outlier exposure"
    - "anomaly detection with generative adversarial networks for multivariate time series"
    - "accurate uncertainties for deep learning using calibrated regression"

Connected through hyperedge HE-4 (induced by source paper "bandits with feedback graphs and switching costs"):
  with members:
    - "online learning with feedback graphs and switching costs"
    - "information directed sampling for stochastic bandits with graph feedback"
    - "thompson sampling for stochastic bandits with graph feedback"
    - "corralling a band of bandit algorithms"
    - "online learning with feedback graphs without the graphs"
    - "online learning with feedback graphs beyond bandits"
    - "nonstochastic multi armed bandits with graph structured feedback"
    - "bandits with switching costs t 2 3 regret"

Connected through hyperedge HE-5 (induced by source paper "binary classification with bounded abstention rate"):
  with members:
    - "active learning for binary classification with abstention"

Connected through hyperedge HE-6 (induced by source paper "a less biased evaluation of out of distribution sample detectors"):
  with members:
    - "out of distribution detection using an ensemble of self supervised leave out classifiers"
    - "deep anomaly detection with outlier exposure"
    - "novelty detection with gan"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "on calibration of modern neural networks"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-7 (induced by source paper "does your model know the digit 6 is not a cat a less biased evaluation of outlier detectors"):
  with members:
    - "novelty detection with gan"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "safer classification by synthesis"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-8 (induced by source paper "safepredict a meta algorithm for machine learning that uses refusals to guarantee correctness"):
  with members:
    - "the extended littlestone s dimension for learning with mistakes and abstentions"
    - "follow the leader if you can hedge if you must"

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)