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: "6thsense a context aware sensor based attack detector for smart devices"
  Abstract: Sensors (e.g., light, gyroscope, accelerotmeter) and sensing enabled applications on a smart device make the applications more user-friendly and efficient. However, the current permission-based sensor management systems of smart devices only focus on certain sensors and any App can get access to other sensors by just accessing the generic sensor API. In this way, attackers can exploit these sensors in numerous ways: they can extract or leak users' sensitive information, transfer malware, or record or steal sensitive information from other nearby devices. In this paper, we propose 6thSense, a context-aware intrusion detection system which enhances the security of smart devices by observing changes in sensor data for different tasks of users and creating a contextual model to distinguish benign and malicious behavior of sensors. 6thSense utilizes three different Machine Learning-based detection mechanisms (i.e., Markov Chain, Naive Bayes, and LMT) to detect malicious behavior associated with sensors. We implemented 6thSense on a sensor-rich Android smart device (i.e., smartphone) and collected data from typical daily activities of 50 real users. Furthermore, we evaluated the performance of 6thSense against three sensor-based threats: (1) a malicious App that can be triggered via a sensor (e.g., light), (2) a malicious App that can leak information via a sensor, and (3) a malicious App that can steal data using sensors. Our extensive evaluations show that the 6thSense framework is an effective and practical approach to defeat growing sensor-based threats with an accuracy above 96% without compromising the normal functionality of the device. Moreover, our framework costs minimal overhead.

Connected through hyperedge HE-1 (induced by source paper "real time analysis of privacy un aware iot applications"):
  with members:
    - "a context aware framework for detecting sensor based threats on smart devices"
    - "aegis a context aware security framework for smart home systems"
    - "program analysis of commodity iot applications for security and privacy challenges and opportunities"
    - "soteria automated iot safety and security analysis"
    - "peek a boo i see your smart home activities even encrypted"
    - "sensitive information tracking in commodity iot"
    - "a survey on sensor based threats to internet of things iot devices and applications"
    - "advertising in the iot era vision and challenges"

Connected through hyperedge HE-2 (induced by source paper "aegis a context aware security framework for smart home systems"):
  with members:
    - "a context aware framework for detecting sensor based threats on smart devices"
    - "cross app interference threats in smart homes categorization detection and handling"
    - "peek a boo i see your smart home activities even encrypted"
    - "sensitive information tracking in commodity iot"
    - "iotdots a digital forensics framework for smart environments"
    - "context aware computing for the internet of things a survey"

Connected through hyperedge HE-3 (induced by source paper "hdmi walk attacking hdmi distribution networks via consumer electronic control protocol"):
  with members:
    - "sensitive information tracking in commodity iot"
    - "advertising in the iot era vision and challenges"
    - "iotdots a digital forensics framework for smart environments"
    - "understanding iot security through the data crystal ball where we are now and where we are going to be"

Connected through hyperedge HE-4 (induced by source paper "healthguard a machine learning based security framework for smart healthcare systems"):
  with members:
    - "a context aware framework for detecting sensor based threats on smart devices"

Connected through hyperedge HE-5 (induced by source paper "sensor defense in software sdi practical software based detection of spoofing attacks on position sensor"):
  with members:
    - "injected and delivered fabricating implicit control over actuation systems by spoofing inertial sensors"

Connected through hyperedge HE-6 (induced by source paper "understanding and mitigating the security risks of voice controlled third party skills on amazon alexa and google home"):
  with members:
    - "continuous authentication for voice assistants"
    - "dolphinattack inaudible voice commands"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "just ask building an architecture for extensible self service spoken language understanding"
    - "neural paraphrase generation with stacked residual lstm networks"
    - "sequence to sequence neural net models for grapheme to phoneme conversion"
    - "a large annotated corpus for learning natural language inference"
    - "your voice assistant is mine how to abuse speakers to steal information and control your phone"

Connected through hyperedge HE-7 (induced by source paper "peek a boo i see your smart home activities even encrypted"):
  with members:
    - "sensitive information tracking in commodity iot"
    - "a survey on sensor based threats to internet of things iot devices and applications"
    - "iotsense behavioral fingerprinting of iot devices"
    - "detection of unauthorized iot devices using machine learning techniques"
    - "spying on the smart home privacy attacks and defenses on encrypted iot traffic"

Connected through hyperedge HE-8 (induced by source paper "a survey on context based co presence detection techniques"):
  with members:
    - "sound proof usable two factor authentication based on ambient sound"
    - "location cheating a security challenge to location based social network services"

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)