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: "targeted adversarial examples for black box audio systems"
  Abstract: The application of deep recurrent networks to audio transcription has led to impressive gains in automatic speech recognition (ASR) systems. Many have demonstrated that small adversarial perturbations can fool deep neural networks into incorrectly predicting a specified target with high confidence. Current work on fooling ASR systems have focused on white-box attacks, in which the model architecture and parameters are known. In this paper, we adopt a black-box approach to adversarial generation, combining the approaches of both genetic algorithms and gradient estimation to solve the task. We achieve a 89.25% targeted attack similarity after 3000 generations while maintaining 94.6% audio file similarity.

Connected through hyperedge HE-1 (induced by source paper "g odel s sentence is an adversarial example but unsolvable"):
  with members:
    - "adversarial robustness may be at odds with simplicity"
    - "adversarial examples are a natural consequence of test error in noise"
    - "text processing like humans do visually attacking and shielding nlp systems"
    - "natural adversarial examples"
    - "on the sensitivity of adversarial robustness to input data distributions"
    - "fooling automated surveillance cameras adversarial patches to attack person detection"
    - "probing neural network comprehension of natural language arguments"
    - "adversarial attacks on deep learning models in natural language processing a survey"

Connected through hyperedge HE-2 (induced by source paper "identifying audio adversarial examples via anomalous pattern detection"):
  with members:
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "robust audio adversarial example for a physical attack"
    - "robust over the air adversarial examples against automatic speech recognition systems"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "adagio interactive experimentation with adversarial attack and defense for audio"
    - "isolated and ensemble audio preprocessing methods for detecting adversarial examples against automatic speech recognition"
    - "characterizing audio adversarial examples using temporal dependency"
    - "audio adversarial examples targeted attacks on speech to text"

Connected through hyperedge HE-3 (induced by source paper "adversarial examples opportunities and challenges"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "atmpa attacking machine learning based malware visualization detection methods via adversarial examples"
    - "hcic hardware assisted control flow integrity checking"
    - "adversarial examples versus cloud based detectors a black box empirical study"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "the malicious use of artificial intelligence forecasting prevention and mitigation"
    - "adversarial attacks against medical deep learning systems"
    - "adversarial patch"

Connected through hyperedge HE-4 (induced by source paper "adversarial examples for models of code"):
  with members:
    - "combating adversarial misspellings with robust word recognition"
    - "gnn film graph neural networks with feature wise linear modulation"
    - "when deep learning met code search"
    - "getafix learning to fix bugs automatically"
    - "structural language models for any code generation"
    - "code2vec learning distributed representations of code"
    - "a general path based representation for predicting program properties"
    - "did you hear that adversarial examples against automatic speech recognition"

Connected through hyperedge HE-5 (induced by source paper "hear no evil see kenansville efficient and transferable black box attacks on speech recognition and voice identification systems"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "practical hidden voice attacks against speech and speaker recognition systems"
    - "commandersong a systematic approach for practical adversarial voice recognition"
    - "adversarial patch"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "adversarial attacks against automatic speech recognition systems via psychoacoustic hiding"
    - "characterizing audio adversarial examples using temporal dependency"
    - "audio adversarial examples targeted attacks on speech to text"

Connected through hyperedge HE-6 (induced by source paper "imperio robust over the air adversarial examples for automatic speech recognition systems"):
  with members:
    - "universal adversarial audio perturbations"
    - "robust audio adversarial example for a physical attack"
    - "a robust approach for securing audio classification against adversarial attacks"
    - "universal adversarial perturbations for speech recognition systems"
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"
    - "practical hidden voice attacks against speech and speaker recognition systems"
    - "on evaluating adversarial robustness"
    - "noise flooding for detecting audio adversarial examples against automatic speech recognition"

Connected through hyperedge HE-7 (induced by source paper "hybrid batch attacks finding black box adversarial examples with limited queries"):
  with members:
    - "hopskipjumpattack a query efficient decision based attack"
    - "there are no bit parts for sign bits in black box attacks"
    - "simple black box adversarial attacks"
    - "stateful detection of black box adversarial attacks"
    - "evading defenses to transferable adversarial examples by translation invariant attacks"
    - "improving black box adversarial attacks with a transfer based prior"
    - "nattack learning the distributions of adversarial examples for an improved black box attack on deep neural networks"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"

Connected through hyperedge HE-8 (induced by source paper "adversarial attacks on copyright detection systems"):
  with members:
    - "robust audio adversarial example for a physical attack"
    - "certified adversarial robustness via randomized smoothing"
    - "daedalus breaking non maximum suppression in object detection via adversarial examples"
    - "adversarial training for free"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "universal adversarial training"
    - "audio adversarial examples targeted attacks on speech to text"
    - "robust physical world attacks on deep learning models"

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)