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: "towards learning and verifying invariants of cyber physical systems by code mutation"
  Abstract: Cyber-physical systems (CPS), which integrate algorithmic control with physical processes, often consist of physically distributed components communicating over a network. A malfunctioning or compromised component in such a CPS can lead to costly consequences, especially in the context of public infrastructure. In this short paper, we argue for the importance of constructing invariants (or models) of the physical behaviour exhibited by CPS, motivated by their applications to the control, monitoring, and attestation of components. To achieve this despite the inherent complexity of CPS, we propose a new technique for learning invariants that combines machine learning with ideas from mutation testing. We present a preliminary study on a water treatment system that suggests the efficacy of this approach, propose strategies for establishing confidence in the correctness of invariants, then summarise some research questions and the steps we are taking to investigate them.

Connected through hyperedge HE-1 (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"
    - "learning from mutants using code mutation to learn and monitor invariants of a cyber physical system"
    - "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"
    - "api design for machine learning software experiences from the scikit learn project"

Connected through hyperedge HE-2 (induced by source paper "learning from mutants using code mutation to learn and monitor invariants of a cyber physical system"):
  with members:
    - "anomaly detection for a water treatment system using unsupervised machine learning"
    - "log based anomaly detection of cps using a statistical method"
    - "towards concolic testing for hybrid systems"

Connected through hyperedge HE-3 (induced by source paper "anomaly detection for a water treatment system using unsupervised machine learning"):
  with members:
    - "log based anomaly detection of cps using a statistical method"
    - "deep structured energy based models for anomaly detection"

Connected through hyperedge HE-4 (induced by source paper "log based anomaly detection of cps using a statistical method"):
  with members:
    - "robust principal component analysis"

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)