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: "secure management of low power fitness trackers"
  Abstract: The increasing popular interest in personal telemetry, also called the Quantified Self or "lifelogging", has induced a popularity surge for wearable personal fitness trackers. Fitness trackers automatically collect sensor data about the user throughout the day, and integrate it into social network accounts. Solution providers have to strike a balance between many constraints, leading to a design process that often puts security in the back seat. Case in point, we reverse engineered and identified security vulnerabilities in Fitbit Ultra and Gammon Forerunner 610, two popular and representative fitness tracker products. We introduce FitBite and GarMax, tools to launch efficient attacks against Fitbit and Garmin. #R##N#We devise SensCrypt, a protocol for secure data storage and communication, for use by makers of affordable and lightweight personal trackers. SensCrypt thwarts not only the attacks we introduced, but also defends against powerful JTAG Read attacks. We have built Sens.io, an Arduino Uno based tracker platform, of similar capabilities but at a fraction of the cost of current solutions. On Sens.io, SensCrypt imposes a negligible write overhead and significantly reduces the end-to-end sync overhead of Fitbit and Garmin.

Connected through hyperedge HE-1 (induced by source paper "a survey on modality characteristics performance evaluation metrics and security for traditional and wearable biometric systems"):
  with members:
    - "survey on synchrophasor data quality and cybersecurity challenges and evaluation of their interdependencies"
    - "secure management of low power fitness trackers"
    - "handwritten signature verification using hand worn devices"
    - "evolution toward 5g multi tier cellular wireless networks an interference management perspective"
    - "an overview of face liveness detection"
    - "enhancing the accuracy of biometric feature extraction fusion using gabor filter and mahalanobis distance algorithm"
    - "bad data injection attack and defense in electricity market using game theory study"
    - "fast computation of the performance evaluation of biometric systems application to multibiometrics"

Connected through hyperedge HE-2 (induced by source paper "iot security an end to end view and case study"):
  with members:
    - "secure management of low power fitness trackers"
    - "internet of things iot a vision architectural elements and future directions"

Connected through hyperedge HE-3 (induced by source paper "understanding iot security through the data crystal ball where we are now and where we are going to be"):
  with members:
    - "secure management of low power fitness trackers"
    - "c flat control flow attestation for embedded systems software"
    - "your voice assistant is mine how to abuse speakers to steal information and control your phone"
    - "context aware computing for the internet of things a survey"

Connected through hyperedge HE-4 (induced by source paper "breaking fitness records without moving reverse engineering and spoofing fitbit"):
  with members:
    - "secure management of low power fitness trackers"

Connected through hyperedge HE-5 (induced by source paper "attacks on fitness trackers revisited a case study of unfit firmware security"):
  with members:
    - "secure management of low power fitness trackers"

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)