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: "aegis a context aware security framework for smart home systems"
  Abstract: Our everyday lives are expanding fast with the introduction of new Smart Home Systems (SHSs). Today, a myriad of SHS devices and applications are widely available to users and have already started to re-define our modern lives. Smart home users utilize the apps to control and automate such devices. Users can develop their own apps or easily download and install them from vendor-specific app markets. App-based SHSs offer many tangible benefits to our lives, but also unfold diverse security risks. Several attacks have already been reported for SHSs. However, current security solutions consider smart home devices and apps individually to detect malicious actions rather than the context of the SHS as a whole. The existing mechanisms cannot capture user activities and sensor-device-user interactions in a holistic fashion. To address these issues, in this paper, we introduce Aegis, a novel context-aware security framework to detect malicious behavior in a SHS. Specifically, Aegis observes the states of the connected smart home entities (sensors and devices) for different user activities and usage patterns in a SHS and builds a contextual model to differentiate between malicious and benign behavior. We evaluated the efficacy and performance of Aegis in multiple smart home settings (i.e., single bedroom, double bedroom, duplex) with real-life users performing day-to-day activities and real SHS devices. We also measured the performance of Aegis against five different malicious behaviors. Our detailed evaluation shows that Aegis can detect malicious behavior in SHS with high accuracy (over 95%) and secure the SHS regardless of the smart home layout, device configuration, installed apps, and enforced user policies. Finally, Aegis achieves minimum overhead in detecting malicious behavior in SHS, ensuring easy deployability in real-life smart environments.

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"
    - "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"
    - "iotdots a digital forensics framework for smart environments"

Connected through hyperedge HE-2 (induced by source paper "a system level behavioral detection framework for compromised cps devices smart grid case"):
  with members:
    - "real time analysis of privacy un aware iot applications"
    - "sensitive information tracking in commodity iot"
    - "detection of compromised smart grid devices with machine learning and convolution techniques"
    - "advertising in the iot era vision and challenges"
    - "iotdots a digital forensics framework for smart environments"
    - "learning from mutants using code mutation to learn and monitor invariants of a cyber physical system"
    - "machine learning methods for attack detection in the smart grid"
    - "smart grid security threats challenges and solutions"

Connected through hyperedge HE-3 (induced by source paper "multi user multi device aware access control system for smart home"):
  with members:
    - "a context aware framework for detecting sensor based threats on smart devices"
    - "a system level behavioral detection framework for compromised cps devices smart grid case"
    - "healthguard a machine learning based security framework for smart healthcare systems"
    - "soteria automated iot safety and security analysis"
    - "peek a boo i see your smart home activities even encrypted"
    - "sensitive information tracking in commodity iot"
    - "advertising in the iot era vision and challenges"
    - "iotdots a digital forensics framework for smart environments"

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)