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: "waca wearable assisted continuous authentication"
  Abstract: One-time login process in conventional authentication systems does not guarantee that the identified user is the actual user throughout the session. However, it is necessary to re-verify the user identity periodically throughout a login session without reducing the user convenience. Continuous authentication can address this issue. However, existing methods are either not reliable or not usable. In this paper, we introduce a usable and reliable method called Wearable Assisted Continuous Authentication (WACA). WACA relies on the sensor based keystroke dynamics, where the authentication data is acquired through the built in sensors of a wearable (e.g., smartwatch) while the user is typing. We implemented the WACA framework and evaluated its performance on real devices with real users. The empirical evaluation of WACA reveals that WACA is feasible and its error rate is as low as 1 percent with 30 seconds of processing time and 2 3% for 20 seconds. The computational overhead is minimal. Furthermore, we tested WACA against different attack scenarios. WACA is capable of identifying insider threats with very high accuracy (99.2%) and also robust against powerful adversaries such as imitation and statistical attackers.

Connected through hyperedge HE-1 (induced by source paper "is your smartband smart enough to know who you are towards continuous physiological authentication in the wild"):
  with members:
    - "deep feature based face detection on mobile devices"

Connected through hyperedge HE-2 (induced by source paper "comparative analysis and framework evaluating mimicry resistant and invisible web authentication schemes"):
  with members:
    - "identification of wearable devices with bluetooth"
    - "sound proof usable two factor authentication based on ambient sound"
    - "mobile device identification via sensor fingerprinting"
    - "touchalytics on the applicability of touchscreen input as a behavioral biometric for continuous authentication"

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.CR(Cryptography and Security)