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: "metrics towards measuring cyber agility"
  Abstract: In cyberspace, evolutionary strategies are commonly used by both attackers and defenders. For example, an attacker’s strategy often changes over the course of time, as new vulnerabilities are discovered and/or mitigated. Similarly, a defender’s strategy changes over time. These changes may or may not be in direct response to a change in the opponent’s strategy. In any case, it is important to have a set of quantitative metrics to characterize and understand the effectiveness of attackers’ and defenders’ evolutionary strategies, which reflect their  cyber agility . Despite its clear importance, few systematic metrics have been developed to quantify the cyber agility of attackers and defenders. In this paper, we propose the first metric framework for measuring cyber agility in terms of the effectiveness of the dynamic evolution of cyber attacks and defenses. The proposed framework is generic and applicable to transform any relevant, quantitative, and/or conventional static security metrics (e.g., false positives and false negatives) into dynamic metrics to capture dynamics of system behaviors. In order to validate the usefulness of the proposed framework, we conduct case studies on measuring the evolution of cyber attacks and defenses using two real-world datasets. We discuss the limitations of the current work and identify future research directions.

Connected through hyperedge HE-1 (induced by source paper "a survey on ethereum systems security vulnerabilities attacks and defenses"):
  with members:
    - "security and privacy on blockchain"
    - "exploring the attack surface of blockchain a systematic overview"
    - "a survey on consensus mechanisms and mining strategy management in blockchain networks"
    - "selfish mining in ethereum"
    - "a survey of distributed consensus protocols for blockchain networks"
    - "finding the greedy prodigal and suicidal contracts at scale"
    - "ethir a framework for high level analysis of ethereum bytecode"
    - "online detection of effectively callback free objects with applications to smart contracts"

Connected through hyperedge HE-2 (induced by source paper "a case study on using deep learning for network intrusion detection"):
  with members:
    - "vuldeepecker a deep learning based system for vulnerability detection"
    - "a novel hierarchical intrusion detection system based on decision tree and rules based models"
    - "characterizing honeypot captured cyber attacks statistical framework and case study"
    - "entity embeddings of categorical variables"
    - "predicting cyber attack rates with extreme values"
    - "preventive and reactive cyber defense dynamics is globally stable"
    - "spatiotemporal patterns and predictability of cyberattacks"
    - "characterizing the power of moving target defense via cyber epidemic dynamics"

Connected through hyperedge HE-3 (induced by source paper "analyzing root causes of intrusion detection false negatives methodology and case study"):
  with members:
    - "benchmarking datasets for anomaly based network intrusion detection kdd cup 99 alternatives"
    - "a taxonomy and survey of intrusion detection system design techniques network threats and datasets"
    - "characterizing honeypot captured cyber attacks statistical framework and case study"
    - "predicting cyber attack rates with extreme values"
    - "an evasion and counter evasion study in malicious websites detection"

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)