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: "malware dynamic analysis evasion techniques a survey"
  Abstract: The Cyber world is plagued with ever-evolving malware that readily infiltrates all defense mechanisms, operates viciously unbeknownst to the user and surreptitiously exfiltrate sensitive data. Understanding the inner workings of such malware provides a leverage to effectively combat them. This understanding, is pursued through dynamic analysis which is conducted manually or automatically. Malware authors accordingly, have devised and advanced evasion techniques to thwart or evade these analyses. In this paper, we present a comprehensive survey on malware dynamic analysis evasion techniques. In addition, we propose a detailed classification of these techniques and further demonstrate how their efficacy hold against different types of detection and analysis approach. Our observations attest that evasive behavior is mostly interested in detecting and evading sandboxes. The primary tactic of such malware we argue is fingerprinting followed by new trends for reverse Turing test tactic which aims at detecting human interaction. Furthermore, we will posit that the current defensive strategies beginning with reactive methods to endeavors for more transparent analysis systems are readily foiled by zero-day fingerprinting techniques or other evasion tactics such as stalling. Accordingly, we would recommend pursuit of more generic defensive strategies with emphasis on path exploration techniques that have the potential to thwart all the evasive tactics.

Connected through hyperedge HE-1 (induced by source paper "malware squid a novel iot malware traffic analysis framework using convolutional neural network and binary visualisation"):
  with members:
    - "a novel malware detection system based on machine learning and binary visualization"
    - "performance comparison of intrusion detection systems and application of machine learning to snort system"
    - "tensorflow a system for large scale machine learning"
    - "social contagion theory examining dynamic social networks and human behavior"
    - "happiness is assortative in online social networks"

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)