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: "evaluation of momentum diverse input iterative fast gradient sign method m di2 fgsm based attack method on mcs 2018 adversarial attacks on black box face recognition system"
  Abstract: The convolutional neural network is the crucial tool for the recent success of deep learning based methods on various computer vision tasks like classification, segmentation, and detection. Convolutional neural networks achieved state-of-the-art performance in these tasks and every day pushing the limit of computer vision and AI. However, adversarial attack on computer vision systems is threatening their application in the real life and in safety-critical applications. Necessarily, Finding adversarial examples are important to detect susceptible models to attack and take safeguard measures to overcome the adversarial attacks. In this regard, MCS 2018 Adversarial Attacks on Black Box Face Recognition challenge aims to facilitate the research of finding new adversarial attack techniques and their effectiveness in generating adversarial examples. In this challenge, the attack"s nature is targeted-attack on the black-box neural network where we have no knowledge about black-block"s inner structure. The attacker must modify a set of five images of a single person so that the neural network miss-classify them as target image which is a set of five images of another person. In this competition, we applied Momentum Diverse Input Iterative Fast Gradient Sign Method (M-DI2-FGSM) to make an adversarial attack on black-box face recognition system. We tested our method on MCS 2018 Adversarial Attacks on Black Box Face Recognition challenge and found competitive result. Our solution got validation score 1.404 which better than baseline score 1.407 and stood 14 place among 132 teams in the leader-board. Further improvement can be achieved by finding improved feature extraction from source image, carefully chosen hyper-parameters, finding improved substitute model of the black-box and better optimization method.

Connected through hyperedge HE-1 (induced by source paper "poba ga perturbation optimized black box adversarial attacks via genetic algorithm"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "identify susceptible locations in medical records via adversarial attacks on deep predictive models"
    - "shapeshifter robust physical adversarial attack on faster r cnn object detector"
    - "adversarial attacks on face detectors using neural net based constrained optimization"
    - "security analysis and enhancement of model compressed deep learning systems under adversarial attacks"
    - "adversarial generative nets neural network attacks on state of the art face recognition"
    - "asp a fast adversarial attack example generation framework based on adversarial saliency prediction"

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.CV(Computer Vision and Pattern Recognition)