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: "walk unlock zero interaction authentication protected with multi modal gait biometrics"
  Abstract: Zero-interaction authentication (ZIA) refers to a form of user-transparent login mechanism using which a terminal (e.g., a desktop computer) can be unlocked by the mere proximity of an authentication token (e.g., a smartphone). Given its appealing usability, ZIA has already been deployed in many real-world applications. However, ZIA contains one major security weakness - unauthorized physical access to the token, e.g., during lunch-time or upon theft, allows the attacker to have unfettered access to the terminal. #R##N#In this paper, we address this gaping vulnerability with ZIA systems by (un)locking the authentication token with the user's walking pattern as she approaches the terminal to access it. Since a user's walking or gait pattern is believed to be unique, only that user (no imposter) would be able to unlock the token to gain access to the terminal in a ZIA session. While walking-based biometrics schemes have been studied in prior literature for other application settings, our main novelty lies in the careful use of: (1) multiple sensors available on the current breed of devices (e.g., accelerometer, gyroscope and magnetometer), and (2) multiple devices carried by the user (in particular, an "in-pocket" smartphone and a "wrist-worn" smartwatch), that all capture unique facets of user's walking pattern. Our contributions are three-fold. First, we introduce, design and implement WUZIA ("Walk-Unlock ZIA"), a multi-modal walking biometrics approach tailored to enhance the security of ZIA systems (still with zero interaction). Second, we demonstrate that WUZIA offers a high degree of detection accuracy, based on multi-sensor and multi-device fusion. Third, we show that WUZIA can resist active attacks that attempt to mimic a user's walking pattern, especially when multiple devices are used.

Connected through hyperedge HE-1 (induced by source paper "perils of zero interaction security in the internet of things"):
  with members:
    - "on the secrecy of publicly observable biometric features security properties of gait for mobile device pairing"
    - "survey and systematization of secure device pairing"
    - "re run repeat reproduce reuse replicate transforming code into scientific contributions"
    - "demo of bandana body area network device to device authentication using natural gait"
    - "the internet of things new interoperability management and security challenges"
    - "sound proof usable two factor authentication based on ambient sound"
    - "context aware computing for the internet of things a survey"

Connected through hyperedge HE-2 (induced by source paper "survey and systematization of secure device pairing"):
  with members:
    - "demo of bandana body area network device to device authentication using natural gait"
    - "rapid user transparent and trustworthy device pairing for d2d enabled mobile crowdsourcing"
    - "challenges in mobile multi device ecosystems"
    - "when theory and reality collide demystifying the effectiveness of ambient sensing for nfc based proximity detection by applying relay attack data"
    - "stash securing transparent authentication schemes using prover side proximity verification"
    - "contextual proximity detection in the face of context manipulating adversaries"
    - "pitfalls in designing zero effort deauthentication opportunistic human observation attacks"
    - "on covert acoustical mesh networks in air"

Connected through hyperedge HE-3 (induced by source paper "tap based user authentication for smartwatches"):
  with members:
    - "illusionpin shoulder surfing resistant authentication using hybrid images"
    - "authenticating users through their arm movement patterns"

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)