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: "deep gamblers learning to abstain with portfolio theory"
  Abstract: We deal with the \textit{selective classification} problem (supervised-learning problem with a rejection option), where we want to achieve the best performance at a certain level of coverage of the data. We transform the original $m$-class classification problem to $(m+1)$-class where the $(m+1)$-th class represents the model abstaining from making a prediction due to uncertainty. Inspired by portfolio theory, we propose a loss function for the selective classification problem based on the doubling rate of gambling. We show that minimizing this loss function has a natural interpretation as maximizing the return of a \textit{horse race}, where a player aims to balance between betting on an outcome (making a prediction) when confident and reserving one's winnings (abstaining) when not confident. This loss function allows us to train neural networks and characterize the uncertainty of prediction in an end-to-end fashion. In comparison with previous methods, our method requires almost no modification to the model inference algorithm or neural architecture. Experimentally, we show that our method can identify both uncertain and outlier data points, and achieves strong results on SVHN and CIFAR10 at various coverages of the data.

Connected through hyperedge HE-1 (induced by source paper "adversarial robustness for code"):
  with members:
    - "certified robustness to adversarial word substitutions"
    - "inferring javascript types using graph neural networks"
    - "achieving verified robustness to symbol substitutions via interval bound propagation"
    - "universal transformers"
    - "adversarial attacks on graph neural networks via meta learning"
    - "structured neural summarization"
    - "code2vec learning distributed representations of code"
    - "adversarial attack on graph structured data"

Connected through hyperedge HE-2 (induced by source paper "self adaptive training beyond empirical risk minimization"):
  with members:
    - "symmetric cross entropy for robust learning with noisy labels"
    - "a jamming transition from under to over parametrization affects generalization in deep learning"
    - "deep double descent where bigger models and more data hurt"
    - "self learning to filter noisy labels with self ensembling"
    - "selectivenet a deep neural network with an integrated reject option"
    - "theoretically principled trade off between robustness and accuracy"
    - "gradient descent with early stopping is provably robust to label noise for overparameterized neural networks"
    - "pytorch an imperative style high performance deep learning library"

Connected through hyperedge HE-3 (induced by source paper "learning not to learn in the presence of noisy labels"):
  with members:
    - "laprop a better way to combine momentum with adaptive gradient"
    - "sgd on neural networks learns functions of increasing complexity"
    - "simple and effective regularization methods for training on noisily labeled data with generalization guarantee"
    - "how does disagreement help generalization against label corruption"
    - "selectivenet a deep neural network with an integrated reject option"
    - "l_dmi an information theoretic noise robust loss function"
    - "gradient descent with early stopping is provably robust to label noise for overparameterized neural networks"
    - "gradient descent finds global minima of deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "learning to switch between machines and humans"):
  with members:
    - "regression under human assistance"
    - "interactive teaching algorithms for inverse reinforcement learning"
    - "learner aware teaching inverse reinforcement learning with preferences and constraints"
    - "selectivenet a deep neural network with an integrated reject option"
    - "the algorithmic automation problem prediction triage and human effort"
    - "exploring applications of deep reinforcement learning for real world autonomous driving systems"
    - "direct uncertainty prediction for medical second opinions"
    - "teaching inverse reinforcement learners via features and demonstrations"

Connected through hyperedge HE-5 (induced by source paper "aleatoric and epistemic uncertainty in machine learning a tutorial introduction"):
  with members:
    - "epistemic uncertainty sampling"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "interpretable adversarial perturbation in input embedding space for text"
    - "evidential deep learning to quantify classification uncertainty"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "on the safety of machine learning cyber physical systems decision sciences and data products"

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)