human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "lipschitz standardization for robust multivariate learning"):
  Abstract: Current trends in machine learning rely on out-of-the-box gradient-based approaches. With the aim of mitigating numerical errors and to improve the convergence of the learning process, a common empirical practice is to standardize or normalize the data. However, there is a lack of theoretical analysis regarding why and when these methods result in an improvement of the learning process. In this work, we first study these methods in the context of black-box variational inference, specifically analyzing the effect that scaling the data has on the smoothness of the optimization landscape. Our analysis shows that no general rule applies in order to decide which of the existing data scaling methods, or even if they, will improve the learning process. Second, we highlight the issues that arise when dealing with multivariate data, due to the discrepancy in smoothness of the likelihood functions for different variables, and the inability to scale discrete data. Finally, we propose a novel Lipschitz standardization, and its extension for discrete data, which overcomes the aforementioned limitations. Specifically, as backed by our experiments, Lipschitz standardization i) favors a fairer learning across different variables in the data; and ii) results in faster and more accurate learning.

Includes members:
  Member "gradient surgery for multi task learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "discor corrective feedback in reinforcement learning via distribution correction")
  Member "handling incomplete heterogeneous data using vaes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unified multi domain learning and data imputation using adversarial autoencoder")
  Member "batch normalization accelerating deep network training by reducing internal covariate shift":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "discrimination aware network pruning for deep model compression")

Question: Please tell me which class the source paper 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)