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 your phish novel techniques for detecting phishing sites and their targets"
  Abstract: Phishing is a major problem on the Web. Despite the significant attention it has received over the years, there has been no definitive solution. While the state-of-the-art solutions have reasonably good performance, they require a large amount of training data and are not adept at detecting phishing attacks against new targets. In this paper, we begin with two core observations: (a) although phishers try to make a phishing webpage look similar to its target, they do not have unlimited freedom in structuring the phishing webpage; and (b) a webpage can be characterized by a small set of key terms; how these key terms are used in different parts of a webpage is different in the case of legitimate and phishing webpages. Based on these observations, we develop a phishing detection system with several notable properties: it is language-independent, can be implemented entirely on client-side, has excellent classification performance and is fast. In addition, we developed a target identification component that can identify the target website that a phishing webpage is attempting to mimic. The target detection component is faster than previously reported systems and can help minimize false positives in our phishing detection system.

Connected through hyperedge HE-1 (induced by source paper "sok a comprehensive reexamination of phishing research from the security perspective"):
  with members:
    - "deltaphish detecting phishing webpages in compromised websites"
    - "defending against phishing attacks taxonomy of methods current issues and future directions"
    - "malicious url detection using machine learning a survey"
    - "an intelligent classification model for phishing email detection"
    - "do users focus on the correct cues to differentiate between phishing and genuine emails"
    - "bit ly malicious deep dive into short url based e crime detection"
    - "an evasion and counter evasion study in malicious websites detection"
    - "phishari automatic realtime phishing detection on twitter"

Connected through hyperedge HE-2 (induced by source paper "safekeeper protecting web passwords using trusted execution environments"):
  with members:
    - "empirical analysis of password reuse and modification across online service"
    - "cash a cost asymmetric secure hash algorithm for optimal password protection"

Connected through hyperedge HE-3 (induced by source paper "detecting homoglyph attacks with a siamese neural network"):
  with members:
    - "hashing for similarity search a survey"

Connected through hyperedge HE-4 (induced by source paper "malicious url detection using machine learning a survey"):
  with members:
    - "online learning a comprehensive survey"
    - "a deep learning approach to fast format agnostic detection of malicious web content"
    - "urlnet learning a url representation with deep learning for malicious url detection"
    - "expose a character level convolutional neural network with embeddings for detecting malicious urls file paths and registry keys"
    - "online deep learning learning deep neural networks on the fly"
    - "gradient episodic memory for continual learning"
    - "identifying malicious web domains using machine learning techniques with online credibility and performance data"
    - "sol a library for scalable online learning algorithms"

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)