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: "machine learning at scale"
  Abstract: It takes skill to build a meaningful predictive model even with the abundance of implementations of modern machine learning algorithms and readily available computing resources. Building a model becomes challenging if hundreds of terabytes of data need to be processed to produce the training data set. In a digital advertising technology setting, we are faced with the need to build thousands of such models that predict user behavior and power advertising campaigns in a 24/7 chaotic real-time production environment. As data scientists, we also have to convince other internal departments critical to implementation success, our management, and our customers that our machine learning system works. In this paper, we present the details of the design and implementation of an automated, robust machine learning platform that impacts billions of advertising impressions monthly. This platform enables us to continuously optimize thousands of campaigns over hundreds of millions of users, on multiple continents, against varying performance objectives.

Connected through hyperedge HE-1 (induced by source paper "using intuition from empirical properties to simplify adversarial training defense"):
  with members:
    - "adversarial logit pairing"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "improving the generalization of adversarial training with domain adaptation"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "magnet a two pronged defense against adversarial examples"
    - "towards evaluating the robustness of neural networks"
    - "adversarial examples in the physical world"
    - "adversarial machine learning at scale"

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)