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: "predicting cyber events by leveraging hacker sentiment"
  Abstract: Recent high-profile cyber-attacks exemplify why organizations need better cyber-defenses. Cyber-threats are hard to accurately predict because attackers usually try to mask their traces. However, they often discuss exploits and techniques on hacking forums. The community behavior of the hackers may provide insights into the groups’ collective malicious activity. We propose a novel approach to predict cyber-events using sentiment analysis. We test our approach using cyber-attack data from two major business organizations. We consider three types of events: malicious software installation, malicious-destination visits, and malicious emails that surmounted the target organizations’ defenses. We construct predictive signals by applying sentiment analysis to hacker forum posts to better understand hacker behavior. We analyze over 400 K posts written between January 2016 and January 2018 on over 100 hacking forums both on the surface and dark web. We find that some forums have significantly more predictive power than others. Sentiment-based models that leverage specific forums can complement state-of-the-art time-series models on forecasting cyber-attacks weeks ahead of the events.

Connected through hyperedge HE-1 (induced by source paper "a compression based framework for the detection of anomalies in heterogeneous data sources"):
  with members:
    - "an adaptive system for detecting malicious queries in web attacks"
    - "shallow and deep networks intrusion detection system a taxonomy and survey"
    - "automatic detection of malware generated domains with recurrent neural models"
    - "current challenges and future research areas for digital forensic investigation"
    - "predicting domain generation algorithms with long short term memory networks"

Connected through hyperedge HE-2 (induced by source paper "characterizing activity on the deep and dark web"):
  with members:
    - "discovering signals from web sources to predict cyber attacks"
    - "darknet and deepnet mining for proactive cybersecurity threat intelligence"

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.CL(Computation and Language)