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: "iotsan fortifying the safety of iot systems"
  Abstract: Today's IoT systems include event-driven smart applications (apps) that interact with sensors and actuators. A problem specific to IoT systems is that buggy apps, unforeseen bad app interactions, or device/communication failures, can cause unsafe and dangerous physical states. Detecting flaws that lead to such states, requires a holistic view of installed apps, component devices, their configurations, and more importantly, how they interact. In this paper, we design IotSan, a novel practical system that uses model checking as a building block to reveal "interaction-level" flaws by identifying events that can lead the system to unsafe states. In building IotSan, we design novel techniques tailored to IoT systems, to alleviate the state explosion associated with model checking. IotSan also automatically translates IoT apps into a format amenable to model checking. Finally, to understand the root cause of a detected vulnerability, we design an attribution mechanism to identify problematic and potentially malicious apps. We evaluate IotSan on the Samsung SmartThings platform. From 76 manually configured systems, IotSan detects 147 vulnerabilities. We also evaluate IotSan with malicious SmartThings apps from a previous effort. IotSan detects the potential safety violations and also effectively attributes these apps as malicious.

Connected through hyperedge HE-1 (induced by source paper "pfirewall semantics aware customizable data flow control for home automation systems"):
  with members:
    - "cross app interference threats in smart homes categorization detection and handling"
    - "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 developer friendly library for smart home iot privacy preserving traffic obfuscation"
    - "user perceptions of smart home iot privacy"
    - "spying on the smart home privacy attacks and defenses on encrypted iot traffic"
    - "closing the blinds four strategies for protecting smart home privacy from network observers"

Connected through hyperedge HE-2 (induced by source paper "manipulating soft tissues by deep reinforcement learning for autonomous robotic surgery"):
  with members:
    - "multi agent deep reinforcement learning with human strategies"
    - "a new tensioning method using deep reinforcement learning for surgical pattern cutting"
    - "deep reinforcement learning for multi agent systems a review of challenges solutions and applications"
    - "a human mixed strategy approach to deep reinforcement learning"
    - "a multi objective deep reinforcement learning framework"
    - "benchmarking deep reinforcement learning for continuous control"
    - "trust region policy optimization"

Connected through hyperedge HE-3 (induced by source paper "helion enabling a natural perspective of home automation"):
  with members:
    - "soteria automated iot safety and security analysis"
    - "sensitive information tracking in commodity iot"
    - "mining android app usages for generating actionable gui based execution scenarios"
    - "a study of data store based home automation"

Connected through hyperedge HE-4 (induced by source paper "program analysis of commodity iot applications for security and privacy challenges and opportunities"):
  with members:
    - "cross app interference threats in smart homes categorization detection and handling"
    - "soteria automated iot safety and security analysis"
    - "incentivized delivery network of iot software updates based on trustless proof of distribution"
    - "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"
    - "understanding iot security through the data crystal ball where we are now and where we are going to be"
    - "internet of things security research a rehash of old ideas or new intellectual challenges"

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)