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: "adversarial attacks on copyright detection systems"
  Abstract: It is well-known that many machine learning models are susceptible to adversarial attacks, in which an attacker evades a classifier by making small perturbations to inputs. This paper discusses how industrial copyright detection tools, which serve a central role on the web, are susceptible to adversarial attacks. We discuss a range of copyright detection systems, and why they are particularly vulnerable to attacks. These vulnerabilities are especially apparent for neural network based systems. As a proof of concept, we describe a well-known music identification method, and implement this system in the form of a neural net. We then attack this system using simple gradient methods. Adversarial music created this way successfully fools industrial systems, including the AudioTag copyright detector and YouTube's Content ID system. Our goal is to raise awareness of the threats posed by adversarial examples in this space, and to highlight the importance of hardening copyright detection systems to attacks.

Connected through hyperedge HE-1 (induced by source paper "freelb enhanced adversarial training for natural language understanding"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "multi task deep neural networks for natural language understanding"
    - "albert a lite bert for self supervised learning of language representations"
    - "robust neural machine translation with doubly adversarial inputs"
    - "roberta a robustly optimized bert pretraining approach"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "feature denoising for improving adversarial robustness"

Connected through hyperedge HE-2 (induced by source paper "pots protective optimization technologies"):
  with members:
    - "discrimination through optimization how facebook s ad delivery can lead to biased outcomes"
    - "explaining explanations in ai"
    - "delayed impact of fair machine learning"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "poisoning attacks to graph based recommender systems"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "fighting offensive language on social media with unsupervised text style transfer"
    - "questioning the assumptions behind fairness solutions"

Connected through hyperedge HE-3 (induced by source paper "adversarial attacks on machine learning systems for high frequency trading"):
  with members:
    - "witchcraft efficient pgd attacks with random step size"
    - "trading via image classification"
    - "adversarial attack on graph structured data"
    - "generating natural language adversarial examples"
    - "universal adversarial training"
    - "fairness in machine learning lessons from political philosophy"
    - "adversarial examples for semantic segmentation and object detection"
    - "universal adversarial perturbations"

Connected through hyperedge HE-4 (induced by source paper "adversarially robust few shot learning a meta learning approach"):
  with members:
    - "meta learning with differentiable convex optimization"
    - "theoretically principled trade off between robustness and accuracy"
    - "a closer look at few shot classification"
    - "adversarially robust transfer learning"
    - "seeing isn t believing practical adversarial attack against object detectors"
    - "feature denoising for improving adversarial robustness"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "practical adversarial attack against object detector"

Connected through hyperedge HE-5 (induced by source paper "freelb enhanced adversarial training for language understanding"):
  with members:
    - "adversarial training for free"
    - "robust neural machine translation with doubly adversarial inputs"
    - "roberta a robustly optimized bert pretraining approach"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "think you have solved question answering try arc the ai2 reasoning challenge"
    - "improving machine reading comprehension with general reading strategies"
    - "characterizing adversarial examples based on spatial consistency information for semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "universal adversarial training"):
  with members:
    - "making an invisibility cloak real world adversarial attacks on object detectors"
    - "playing the game of universal adversarial perturbations"
    - "adversarial patch"
    - "ask acquire and attack data free uap generation using class impressions"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "defending against universal perturbations with shared adversarial training"
    - "generating adversarial examples with adversarial networks"
    - "generalizable data free objective for crafting universal adversarial perturbations"

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)