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: "statistical meta analysis of presentation attacks for secure multibiometric systems"
  Abstract: Prior work has shown that multibiometric systems are vulnerable to presentation attacks, assuming that their matching score distribution is identical to that of genuine users, without fabricating any fake trait. We have recently shown that this assumption is not representative of current fingerprint and face presentation attacks, leading one to overestimate the vulnerability of multibiometric systems, and to design less effective fusion rules. In this paper, we overcome these limitations by proposing a statistical meta-model of face and fingerprint presentation attacks that characterizes a wider family of fake score distributions, including distributions of known and, potentially, unknown attacks. This allows us to perform a thorough security evaluation of multibiometric systems against presentation attacks, quantifying how their vulnerability may vary also under attacks that are different from those considered during design, through an uncertainty analysis. We empirically show that our approach can reliably predict the performance of multibiometric systems even under never-before-seen face and fingerprint presentation attacks, and that the secure fusion rules designed using our approach can exhibit an improved trade-off between the performance in the absence and in the presence of attack. We finally argue that our method can be extended to other biometrics besides faces and fingerprints.

Connected through hyperedge HE-1 (induced by source paper "wild patterns ten years after the rise of adversarial machine learning"):
  with members:
    - "poisoning behavioral malware clustering"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "is feature selection secure against training data poisoning"
    - "is data clustering in adversarial settings secure"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "certified defenses for data poisoning attacks"
    - "deltaphish detecting phishing webpages in compromised websites"

Connected through hyperedge HE-2 (induced by source paper "inference based similarity search in randomized montgomery domains for privacy preserving biometric identification"):
  with members:
    - "learning robust deep face representation"
    - "learning face representation from scratch"
    - "fast exact search in hamming space with multi index hashing"
    - "secure biometrics concepts authentication architectures and challenges"

Connected through hyperedge HE-3 (induced by source paper "how wrong am i studying adversarial examples and their impact on uncertainty in gaussian process machine learning models"):
  with members:
    - "sok security and privacy in machine learning"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "dropout inference in bayesian neural networks with alpha divergences"
    - "deep network guided proof search"
    - "is deep learning safe for robot vision adversarial examples against the icub humanoid"
    - "simple black box adversarial perturbations for deep networks"
    - "adversarial examples attacks and defenses for deep learning"
    - "adversarial perturbations against deep neural networks for malware classification"

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)