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: "setting the threshold for high throughput detectors a mathematical approach for ensembles of dynamic heterogeneous probabilistic anomaly detectors"
  Abstract: Anomaly detection (AD) has garnered ample attention in security research, as such algorithms complement existing signature-based methods but promise detection of never-before-seen attacks. Cyber operations manage a high volume of heterogeneous log data; hence, AD in such operations involves multiple (e.g., per IP, per data type) ensembles of detectors modeling heterogeneous characteristics (e.g., rate, size, type) often with adaptive online models producing alerts in near real time. Because of high data volume, setting the threshold for each detector in such a system is an essential yet underdeveloped configuration issue that, if slightly mistuned, can leave the system useless, either producing a myriad of alerts and flooding downstream systems, or giving none. In this work, we build on the foundations of Ferragut et al. to provide a set of rigorous results for understanding the relationship between threshold values and alert quantities, and we propose an algorithm for setting the threshold in practice. Specifically, we give an algorithm for setting the threshold of multiple, heterogeneous, possibly dynamic detectors completely a priori, in principle. Indeed, if the underlying distribution of the incoming data is known (closely estimated), the algorithm provides provably manageable thresholds. If the distribution is unknown (e.g., has changed over time) our analysis reveals how the model distribution differs from the actual distribution, indicating a period of model refitting is necessary. We provide empirical experiments showing the efficacy of the capability by regulating the alert rate of a system with $\approx$2,500 adaptive detectors scoring over 1.5M events in 5 hours. Further, we demonstrate on the real network data and detection framework of Harshaw et al. the alternative case, showing how the inability to regulate alerts indicates the detection model is a bad fit to the data.

Connected through hyperedge HE-1 (induced by source paper "should i raise the red flag a comprehensive survey of anomaly scoring methods toward mitigating false alarms"):
  with members:
    - "active anomaly detection via ensembles"
    - "detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "deep learning for iot big data and streaming analytics a survey"
    - "deep learning for time series analysis"
    - "a meta analysis of the anomaly detection problem"
    - "systematic construction of anomaly detection benchmarks from real data"
    - "evaluating real time anomaly detection algorithms the numenta anomaly benchmark"

Connected through hyperedge HE-2 (induced by source paper "a survey of intrusion detection systems leveraging host data"):
  with members:
    - "malware detection on general purpose computers using power consumption monitoring a proof of concept and case study"
    - "automated behavioral analysis of malware a case study of wannacry ransomware"
    - "identifying ecus using inimitable characteristics of signals in controller area networks"
    - "graphprints towards a graph analytic method for network anomaly detection"

Connected through hyperedge HE-3 (induced by source paper "towards malware detection via cpu power consumption data collection design and analytics extended version"):
  with members:
    - "malware detection on general purpose computers using power consumption monitoring a proof of concept and case study"
    - "graphprints towards a graph analytic method for network anomaly detection"

Connected through hyperedge HE-4 (induced by source paper "forming ideas interactive data exploration analysis system"):
  with members:
    - "automated behavioral analysis of malware a case study of wannacry ransomware"

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)