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: "minimizing the maximal loss how and why"
  Abstract: A commonly used learning rule is to approximately minimize the \emph{average} loss over the training set. Other learning algorithms, such as AdaBoost and hard-SVM, aim at minimizing the \emph{maximal} loss over the training set. The average loss is more popular, particularly in deep learning, due to three main reasons. First, it can be conveniently minimized using online algorithms, that process few examples at each iteration. Second, it is often argued that there is no sense to minimize the loss on the training set too much, as it will not be reflected in the generalization loss. Last, the maximal loss is not robust to outliers. In this paper we describe and analyze an algorithm that can convert any online algorithm to a minimizer of the maximal loss. We prove that in some situations better accuracy on the training set is crucial to obtain good performance on unseen examples. Last, we propose robust versions of the approach that can handle outliers.

Connected through hyperedge HE-1 (induced by source paper "distribution agnostic model agnostic meta learning"):
  with members:
    - "online meta learning on non convex setting"
    - "online meta learning"
    - "agnostic federated learning"
    - "adversarial attacks on graph neural networks via meta learning"
    - "on the convergence theory of gradient based model agnostic meta learning algorithms"
    - "on first order meta learning algorithms"
    - "reptile a scalable metalearning algorithm"
    - "how to train your maml"

Connected through hyperedge HE-2 (induced by source paper "mixed strategies for robust optimization of unknown objectives"):
  with members:
    - "cooperation aware reinforcement learning for merging in dense traffic"
    - "bayesian optimisation under uncertain inputs"
    - "no regret learning in unknown games with correlated payoffs"
    - "safe intersection management for mixed transportation systems with human driven and autonomous vehicles"
    - "distributionally robust submodular maximization"
    - "hierarchical game theoretic planning for autonomous vehicles"
    - "practical bayesian optimization in the presence of outliers"
    - "robust optimization for non convex objectives"

Connected through hyperedge HE-3 (induced by source paper "ipboost non convex boosting via integer programming"):
  with members:
    - "boolean decision rules via column generation"
    - "evaluating robustness of neural networks with mixed integer programming"
    - "optimal generalized decision trees via integer programming"

Connected through hyperedge HE-4 (induced by source paper "a simple and effective framework for pairwise deep metric learning"):
  with members:
    - "hybrid attention based decoupled metric learning for zero shot image retrieval"
    - "multi similarity loss with general pair weighting for deep metric learning"
    - "revisiting metric learning for few shot image classification"
    - "deep metric learning with bier boosting independent embeddings robustly"
    - "large scale distance metric learning with uncertainty"
    - "hard aware point to set deep metric for person re identification"
    - "deep metric learning with hierarchical triplet loss"
    - "attention based ensemble for deep metric learning"

Connected through hyperedge HE-5 (induced by source paper "adasample adaptive sampling of hard positives for descriptor learning"):
  with members:
    - "hardness aware deep metric learning"
    - "pytorch an imperative style high performance deep learning library"
    - "sosnet second order similarity regularization for local descriptor learning"
    - "arcface additive angular margin loss for deep face recognition"
    - "not all samples are created equal deep learning with importance sampling"
    - "local descriptors optimized for average precision"
    - "geodesc learning local descriptors by integrating geometry constraints"
    - "sampling matters in deep embedding learning"

Connected through hyperedge HE-6 (induced by source paper "robust attacks against multiple classifiers"):
  with members:
    - "adversarially robust generalization requires more data"
    - "black box adversarial attacks with limited queries and information"
    - "certified defenses against adversarial examples"
    - "certified defenses for data poisoning attacks"
    - "robust optimization for non convex objectives"
    - "decoupling when to update from how to update"
    - "robustness to adversarial examples through an ensemble of specialists"
    - "adversarial example defenses ensembles of weak defenses are not strong"

Connected through hyperedge HE-7 (induced by source paper "closing the gap towards end to end autonomous vehicle system"):
  with members:
    - "learning end to end autonomous driving using guided auxiliary supervision"
    - "deep density networks and uncertainty in recommender systems"
    - "end to end multi modal multi task vehicle control for self driving cars with visual perception"
    - "end to end learning of driving models with surround view cameras and route planners"
    - "focal loss for dense object detection"
    - "failures of gradient based deep learning"
    - "on a formal model of safe and scalable self driving cars"
    - "end to end driving via conditional imitation learning"

Connected through hyperedge HE-8 (induced by source paper "adaptive sampling for stochastic risk averse learning"):
  with members:
    - "multi player bandits the adversarial case"
    - "distributionally robust optimization and generalization in kernel methods"
    - "introduction to online convex optimization"
    - "exact sampling of determinantal point processes with sublinear time preprocessing"
    - "fairness risk measures"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "monte carlo markov chain algorithms for sampling strongly rayleigh distributions and determinantal point processes"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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)