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: "the efficacy of shield under different threat models"
  Abstract: In this appraisal paper, we evaluate the efficacy of SHIELD, a compression-based defense framework for countering adversarial attacks on image classification models, which was published at KDD 2018. Here, we consider alternative threat models not studied in the original work, where we assume that an adaptive adversary is aware of the ensemble defense approach, the defensive pre-processing, and the architecture and weights of the models used in the ensemble. We define scenarios with varying levels of threat and empirically analyze the proposed defense by varying the degree of information available to the attacker, spanning from a full white-box attack to the gray-box threat model described in the original work. To evaluate the robustness of the defense against an adaptive attacker, we consider the targeted-attack success rate of the Projected Gradient Descent (PGD) attack, which is a strong gradient-based adversarial attack proposed in adversarial machine learning research. We also experiment with training the SHIELD ensemble from scratch, which is different from re-training using a pre-trained model as done in the original work. We find that the targeted PGD attack has a success rate of 64.3% against the original SHIELD ensemble in the full white box scenario, but this drops to 48.9% if the models used in the ensemble are trained from scratch instead of being retrained. Our experiments further reveal that an ensemble whose models are re-trained indeed have higher correlation in the cosine similarity space, and models that are trained from scratch are less vulnerable to targeted attacks in the white-box and gray-box scenarios.

Connected through hyperedge HE-1 (induced by source paper "tensorshield tensor based defense against adversarial attacks on images"):
  with members:
    - "shield fast practical defense and vaccination for deep learning using jpeg compression"
    - "technical report on the cleverhans v2 1 0 adversarial examples library"
    - "tensorly tensor learning in python"
    - "adversarial examples in the physical world"
    - "distillation as a defense to adversarial perturbations against deep neural networks"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-2 (induced by source paper "deepsearch simple and effective blackbox fuzzing of deep neural networks"):
  with members:
    - "towards improved testing for deep learning"
    - "algorithms for verifying deep neural networks"
    - "machine learning testing survey landscapes and horizons"
    - "on evaluating adversarial robustness"
    - "formal security analysis of neural networks using symbolic intervals"
    - "a dual approach to scalable verification of deep networks"
    - "symbolic execution for deep neural networks"
    - "deepgauge multi granularity testing criteria for deep learning systems"

Connected through hyperedge HE-3 (induced by source paper "on evaluating adversarial robustness"):
  with members:
    - "adversarial examples are a natural consequence of test error in noise"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "generative adversarial examples"
    - "adversarial risk and the dangers of evaluating against weak attacks"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "benchmarking neural network robustness to common corruptions and surface variations"
    - "logit pairing methods can fool gradient based attacks"
    - "adversarial examples that fool both computer vision and time limited humans"

Connected through hyperedge HE-4 (induced by source paper "perfectly parallel fairness certification of neural networks"):
  with members:
    - "probabilistic verification of fairness properties via concentration"
    - "a provable defense for deep residual networks"
    - "a survey on bias and fairness in machine learning"
    - "analyzing deep neural networks with symbolic propagation towards higher precision and faster verification"
    - "deepsearch simple and effective blackbox fuzzing of deep neural networks"
    - "formal security analysis of neural networks using symbolic intervals"
    - "symbolic execution for deep neural networks"
    - "concolic testing for deep neural networks"

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)