human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a secure mobile authentication alternative to biometrics"):
  Abstract: Biometrics are widely used for authentication in consumer devices and business settings as they provide sufficiently strong security instant verification and convenience for users. However, biometrics are hard to keep secret, stolen biometrics pose lifelong security risks to users as they cannot be reset and re-issued, and transactions authenticated by biometrics across different systems are linkable and traceable back to the individual identity. In addition, their cost-benefit analysis does not include personal implications to users, who are least prepared for the imminent negative outcomes, and are not often given equally convenient alternative authentication options.   We introduce ai.lock, a secret image based authentication method for mobile devices which uses an imaging sensor to reliably extract authentication credentials similar to biometrics. Despite lacking the regularities of biometric image features, we show that ai.lock consistently extracts features across authentication attempts from general user captured images, to reconstruct credentials that can match and exceed the security of biometrics (EER = 0.71%). ai.lock only stores a "hash" of the object's image. We measure the security of ai.lock against brute force attacks on more than 3.5 billion authentication instances built from more than 250,000 images of real objects, and 100,000 synthetically generated images using a generative adversarial network trained on object images. We show that the ai.lock Shannon entropy is superior to a fingerprint based authentication built into popular mobile devices.

Includes members:
  Member "video liveness for citizen journalism attacks and defenses":
    also connected through other hyperedges:
      (none)
  Member "camera based two factor authentication through mobile and wearable devices":
    also connected through other hyperedges:
      (none)
  Member "yfcc100m the new data in multimedia research":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "in defense of grid features for visual question answering")
  Member "tensorflow a system for large scale machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "armageddon cache attacks on mobile devices":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "nowhere to hide cross modal identity leakage between biometrics and devices")
  Member "unsupervised representation learning with deep convolutional generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "sound proof usable two factor authentication based on ambient sound":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "short range audio channels security survey of mechanisms applications and research challenges")
  Member "deep representations for iris face and fingerprint spoofing detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "biometric presentation attack detection beyond the visible spectrum")

Question: Please tell me which class the source paper 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.CR(Cryptography and Security)