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: "learning from mutants using code mutation to learn and monitor invariants of a cyber physical system"
  Abstract: Cyber-physical systems (CPS) consist of sensors, actuators, and controllers all communicating over a network; if any subset becomes compromised, an attacker could cause significant damage. With access to data logs and a model of the CPS, the physical effects of an attack could potentially be detected before any damage is done. Manually building a model that is accurate enough in practice, however, is extremely difficult. In this paper, we propose a novel approach for constructing models of CPS automatically, by applying supervised machine learning to data traces obtained after systematically seeding their software components with faults ("mutants"). We demonstrate the efficacy of this approach on the simulator of a real-world water purification plant, presenting a framework that automatically generates mutants, collects data traces, and learns an SVM-based model. Using cross-validation and statistical model checking, we show that the learnt model characterises an invariant physical property of the system. Furthermore, we demonstrate the usefulness of the invariant by subjecting the system to 55 network and code-modification attacks, and showing that it can detect 85% of them from the data logs generated at runtime.

Connected through hyperedge HE-1 (induced by source paper "conaml constrained adversarial machine learning for cyber physical systems"):
  with members:
    - "textbugger generating adversarial text against real world applications"
    - "dynamic detection of false data injection attack in smart grid using deep learning"
    - "adversarial attacks and defences a survey"
    - "adversarial regression for detecting attacks in cyber physical systems"
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"
    - "a deep learning based framework for conducting stealthy attacks in industrial control systems"
    - "anomaly detection for a water treatment system using unsupervised machine learning"
    - "deeptest automated testing of deep neural network driven autonomous cars"

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"
    - "aegis a context aware security framework for smart home systems"
    - "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"
    - "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 "learning guided network fuzzing for testing cyber physical system defences"):
  with members:
    - "detecting zero day controller hijacking attacks on the power grid with enhanced deep learning"
    - "assessing the effectiveness of attack detection at a hackfest on industrial control systems"
    - "anomaly detection for a water treatment system using unsupervised machine learning"
    - "log based anomaly detection of cps using a statistical method"
    - "a formal approach to cyber physical attacks"
    - "towards concolic testing for hybrid systems"
    - "towards learning and verifying invariants of cyber physical systems by code mutation"
    - "api design for machine learning software experiences from the scikit learn project"

Connected through hyperedge HE-4 (induced by source paper "anomaly detection for industrial control systems using sequence to sequence neural networks"):
  with members:
    - "mad gan multivariate anomaly detection for time series data with generative adversarial networks"
    - "on the convergence of adam and beyond"
    - "anomaly detection for water treatment system based on neural network with automatic architecture optimization"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-5 (induced by source paper "investigation of cyber attacks on a water distribution system"):
  with members:
    - "assessing the effectiveness of attack detection at a hackfest on industrial control systems"
    - "cyber physical systems security a survey"

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.SE(Software Engineering)