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: "phishari automatic realtime phishing detection on twitter"
  Abstract: With the advent of online social media, phishers have started using social networks like Twitter, Facebook, and Foursquare to spread phishing scams. Twitter is an immensely popular micro-blogging network where people post short messages of 140 characters called tweets. It has over 100 million active users who post about 200 million tweets everyday. Phishers have started using Twitter as a medium to spread phishing because of this vast information dissemination. Further, it is difficult to detect phishing on Twitter unlike emails because of the quick spread of phishing links in the network, short size of the content, and use of URL obfuscation to shorten the URL. Our technique, PhishAri, detects phishing on Twitter in realtime. We use Twitter specific features along with URL features to detect whether a tweet posted with a URL is phishing or not. Some of the Twitter specific features we use are tweet content and its characteristics like length, hashtags, and mentions. Other Twitter features used are the characteristics of the Twitter user posting the tweet such as age of the account, number of tweets, and the follower-followee ratio. These Twitter specific features coupled with URL based features prove to be a strong mechanism to detect phishing tweets. We use machine learning classification techniques and detect phishing tweets with an accuracy of 92.52%. We have deployed our system for end-users by providing an easy to use Chrome browser extension which works in realtime and classifies a tweet as phishing or safe. We show that we are able to detect phishing tweets at zero hour with high accuracy which is much faster than public blacklists and as well as Twitter's own defense mechanism to detect malicious content. To the best of our knowledge, this is the first realtime, comprehensive and usable system to detect phishing on Twitter.

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"
    - "know your phish novel techniques for detecting phishing sites and their targets"
    - "bit ly malicious deep dive into short url based e crime detection"
    - "an evasion and counter evasion study in malicious websites detection"

Connected through hyperedge HE-2 (induced by source paper "fake news identification on twitter with hybrid cnn and rnn models"):
  with members:
    - "lstm a search space odyssey"
    - "cnn rnn a unified framework for multi label image classification"
    - "learning reporting dynamics during breaking news for rumour detection in social media"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "manipulation and abuse on social media by emilio ferrara with ching man au yeung as coordinator"
    - "a survey of location inference techniques on twitter"
    - "tweetcred real time credibility assessment of content on twitter"
    - "deep visual semantic alignments for generating image descriptions"

Connected through hyperedge HE-3 (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"

Connected through hyperedge HE-4 (induced by source paper "defending against phishing attacks taxonomy of methods current issues and future directions"):
  with members:
    - "serious games for cyber security education"
    - "using syntactic features for phishing detection"
    - "user centred security education a game design to thwart phishing attacks"
    - "phishing dynamic evolving neural fuzzy framework for online detection zero day phishing email"
    - "internet of things iot a vision architectural elements and future directions"

Connected through hyperedge HE-5 (induced by source paper "hiding in plain sight the anatomy of malicious facebook pages"):
  with members:
    - "tweetcred real time credibility assessment of content on twitter"
    - "uncovering social network sybils in the wild"

Connected through hyperedge HE-6 (induced by source paper "online social networks threats and solutions"):
  with members:
    - "organization mining using online social networks"
    - "friend or foe fake profile identification in online social networks"
    - "social turing tests crowdsourcing sybil detection"

Connected through hyperedge HE-7 (induced by source paper "tweetcred real time credibility assessment of content on twitter"):
  with members:
    - "truthy mapping the spread of astroturf in microblog streams"

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.SI(Social and Information Networks)