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: "robust optimization over multiple domains"
  Abstract: In this work, we study the problem of learning a single model for multiple domains. Unlike the conventional machine learning scenario where each domain can have the corresponding model, multiple domains (i.e., applications/users) may share the same machine learning model due to maintenance loads in cloud computing services. For example, a digit-recognition model should be applicable to hand-written digits, house numbers, car plates, etc. Therefore, an ideal model for cloud computing has to perform well at each applicable domain. To address this new challenge from cloud computing, we develop a framework of robust optimization over multiple domains. In lieu of minimizing the empirical risk, we aim to learn a model optimized to the adversarial distribution over multiple domains. Hence, we propose to learn the model and the adversarial distribution simultaneously with the stochastic algorithm for efficiency. Theoretically, we analyze the convergence rate for convex and non-convex models. To our best knowledge, we first study the convergence rate of learning a robust non-convex model with a practical algorithm. Furthermore, we demonstrate that the robustness of the framework and the convergence rate can be further enhanced by appropriate regularizers over the adversarial distribution. The empirical study on real-world fine-grained visual categorization and digits recognition tasks verifies the effectiveness and efficiency of the proposed framework.

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 "beyond adversarial training min max optimization in adversarial attack and defense"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "robust neural networks using randomized adversarial training"
    - "audio adversarial examples targeted attacks on speech to text"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial attacks on neural network policies"
    - "tactics of adversarial attack on deep reinforcement learning agents"
    - "magnet a two pronged defense against adversarial examples"

Connected through hyperedge HE-3 (induced by source paper "topology attack and defense for graph neural networks an optimization perspective"):
  with members:
    - "adversarial attacks on graph neural networks via meta learning"
    - "interpreting adversarial examples by activation promotion and suppression"
    - "adversarial attack on graph structured data"
    - "adversarial attacks on node embeddings via graph poisoning"
    - "how powerful are graph neural networks"
    - "structured adversarial attack towards general implementation and better interpretability"
    - "audio adversarial examples targeted attacks on speech to text"
    - "robust optimization for non convex objectives"

Connected through hyperedge HE-4 (induced by source paper "min max optimization without gradients convergence and applications to adversarial ml"):
  with members:
    - "beyond adversarial training min max optimization in adversarial attack and defense"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "on the convergence and robustness of training gans with regularized optimal transport"
    - "spectral signatures in backdoor attacks"
    - "caad 2018 iterative ensemble adversarial attack"
    - "approximating nash equilibria for black box games a bayesian optimization approach"
    - "towards distributed coevolutionary gans"
    - "certified defenses for data poisoning attacks"

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)