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: "poised spotting twitter spam off the beaten paths"
  Abstract: Cybercriminals have found in online social networks a propitious medium to spread spam and malicious content. Existing techniques for detecting spam include predicting the trustworthiness of accounts and analyzing the content of these messages. However, advanced attackers can still successfully evade these defenses. #R##N#Online social networks bring people who have personal connections or share common interests to form communities. In this paper, we first show that users within a networked community share some topics of interest. Moreover, content shared on these social network tend to propagate according to the interests of people. Dissemination paths may emerge where some communities post similar messages, based on the interests of those communities. Spam and other malicious content, on the other hand, follow different spreading patterns. #R##N#In this paper, we follow this insight and present POISED, a system that leverages the differences in propagation between benign and malicious messages on social networks to identify spam and other unwanted content. We test our system on a dataset of 1.3M tweets collected from 64K users, and we show that our approach is effective in detecting malicious messages, reaching 91% precision and 93% recall. We also show that POISED's detection is more comprehensive than previous systems, by comparing it to three state-of-the-art spam detection systems that have been proposed by the research community in the past. POISED significantly outperforms each of these systems. Moreover, through simulations, we show how POISED is effective in the early detection of spam messages and how it is resilient against two well-known adversarial machine learning attacks.

Connected through hyperedge HE-1 (induced by source paper "ties temporal interaction embeddings for enhancing social media integrity at facebook"):
  with members:
    - "predicting dynamic embedding trajectory in temporal interaction networks"
    - "a survey of the recent architectures of deep convolutional neural networks"
    - "pytorch biggraph a large scale graph embedding system"
    - "deep learning for hate speech detection in tweets"
    - "attention is all you need"
    - "in a world that counts clustering and detecting fake social engagement at scale"
    - "a critical review of recurrent neural networks for sequence learning"

Connected through hyperedge HE-2 (induced by source paper "detecting cyberbullying and cyberaggression in social media"):
  with members:
    - "mean birds detecting aggression and bullying on twitter"
    - "measuring gamergate a tale of hate sexism and bullying"
    - "hate is not binary studying abusive behavior of gamergate on twitter"
    - "kek cucks and god emperor trump a measurement study of 4chan s politically incorrect forum and its effects on the web"
    - "a longitudinal measurement study of 4chan s politically incorrect forum and its effect on the web"
    - "deep residual learning for image recognition"
    - "exploring cyberbullying and other toxic behavior in team competition online games"
    - "deep learning and the information bottleneck principle"

Connected through hyperedge HE-3 (induced by source paper "you know what to do proactive detection of youtube videos targeted by coordinated hate attacks"):
  with members:
    - "what is gab a bastion of free speech or an alt right echo chamber"
    - "the effect of extremist violence on hateful speech online"
    - "community interaction and conflict on the web"
    - "peer to peer hate hate speech instigators and their targets"
    - "hate lingo a target based linguistic analysis of hate speech in social media"
    - "opinion conflicts an effective route to detect incivility in twitter"
    - "mean birds detecting aggression and bullying on twitter"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"

Connected through hyperedge HE-4 (induced by source paper "collective classification of spam campaigners on twitter a hierarchical meta path based approach"):
  with members:
    - "dial one for scam a large scale analysis of technical support scams"
    - "botornot a system to evaluate social bots"
    - "detecting smart spammers on social network a topic model approach"
    - "the rise of social bots"
    - "towards detecting compromised accounts on social networks"
    - "detecting criminal organizations in mobile phone networks"
    - "meta path based collective classification in heterogeneous information networks"
    - "hetesim a general framework for relevance measure in heterogeneous networks"

Connected through hyperedge HE-5 (induced by source paper "lobo evaluation of generalization deficiencies in twitter bot classifiers"):
  with members:
    - "discovery of the twitter bursty botnet"
    - "the paradigm shift of social spambots evidence theories and tools for the arms race"
    - "botornot a system to evaluate social bots"
    - "the darpa twitter bot challenge"
    - "paying for likes understanding facebook like fraud using honeypots"

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)