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: "know abnormal find evil frequent pattern mining for ransomware threat hunting and intelligence"
  Abstract: Emergence of crypto-ransomware has significantly changed the cyber threat landscape. A crypto ransomware removes data custodian access by encrypting valuable data on victims' computers and requests a ransom payment to reinstantiate custodian access by decrypting data. Timely detection of ransomware very much depends on how quickly and accurately system logs can be mined to hunt abnormalities and stop the evil. In this paper we first setup an environment to collect activity logs of 517 Locky ransomware samples, 535 Cerber ransomware samples and 572 samples of TeslaCrypt ransomware. We utilize Sequential Pattern Mining to find Maximal Frequent Patterns (MFP) of activities within different ransomware families as candidate features for classification using J48, Random Forest, Bagging and MLP algorithms. We could achieve 99% accuracy in detecting ransomware instances from goodware samples and 96.5% accuracy in detecting family of a given ransomware sample. Our results indicate usefulness and practicality of applying pattern mining techniques in detection of good features for ransomware hunting. Moreover, we showed existence of distinctive frequent patterns within different ransomware families which can be used for identification of a ransomware sample family for building intelligence about threat actors and threat profile of a given target.

Connected through hyperedge HE-1 (induced by source paper "a blockchain based framework for detecting malicious mobile applications in app stores"):
  with members:
    - "empirical vulnerability analysis of automated smart contracts security testing on blockchains"
    - "a model for android and ios applications risk calculation cvss analysis and enhancement using case control studies"
    - "a cyber kill chain based taxonomy of banking trojans for evolutionary computational intelligence"
    - "forensics analysis of android mobile voip apps"

Connected through hyperedge HE-2 (induced by source paper "automated ransomware behavior analysis pattern extraction and early detection"):
  with members:
    - "credit default mining using combined machine learning and heuristic approach"
    - "how do information security workers use host data a summary of interviews with security analysts"
    - "automated behavioral analysis of malware a case study of wannacry ransomware"

Connected through hyperedge HE-3 (induced by source paper "emerging from the cloud a bibliometric analysis of cloud forensics studies"):
  with members:
    - "greening cloud enabled big data storage forensics syncany as a case study"
    - "internet of things security and forensics challenges and opportunities"
    - "forensic investigation of p2p cloud storage bittorrent sync as a case study"
    - "cloud storage forensics analysis of data remnants on spideroak justcloud and pcloud"
    - "a cyber kill chain based taxonomy of banking trojans for evolutionary computational intelligence"
    - "investigating storage as a service cloud platform pcloud as a case study"
    - "cloud storage forensic hubic as a case study"
    - "calm before the storm the challenges of cloud computing in digital forensics"

Connected through hyperedge HE-4 (induced by source paper "a model for android and ios applications risk calculation cvss analysis and enhancement using case control studies"):
  with members:
    - "greening cloud enabled big data storage forensics syncany as a case study"
    - "internet of things security and forensics challenges and opportunities"
    - "mobile phone forensics an investigative framework based on user impulsivity and secure collaboration errors"
    - "a cyber kill chain based taxonomy of banking trojans for evolutionary computational intelligence"
    - "forensics analysis of android mobile voip apps"
    - "an android cloud storage apps forensic taxonomy"
    - "forensic investigation of social media and instant messaging services in firefox os facebook twitter google telegram openwapp and line as case studies"

Connected through hyperedge HE-5 (induced by source paper "leveraging machine learning techniques for windows ransomware network traffic detection"):
  with members:
    - "greening cloud enabled big data storage forensics syncany as a case study"
    - "cloudme forensics a case of big data forensic investigation"
    - "a cyber kill chain based taxonomy of banking trojans for evolutionary computational intelligence"
    - "exploit kits the production line of the cybercrime economy"
    - "automated dynamic analysis of ransomware benefits limitations and use for detection"
    - "automated detection and classification of cryptographic algorithms in binary programs through machine learning"

Connected through hyperedge HE-6 (induced by source paper "leveraging support vector machine for opcode density based detection of crypto ransomware"):
  with members:
    - "leveraging machine learning techniques for windows ransomware network traffic detection"
    - "automated dynamic analysis of ransomware benefits limitations and use for 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)