human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "deep neural networks for bot detection"):
  Abstract: Abstract   The problem of detecting bots, automated social media accounts governed by software but disguising as human users, has strong implications. For example, bots have been used to sway political elections by distorting online discourse, to manipulate the stock market, or to push anti-vaccine conspiracy theories that may have caused health epidemics. Most techniques proposed to date detect bots at the account level, by processing large amounts of social media posts, and leveraging information from network structure, temporal dynamics, sentiment analysis, etc. In this paper, we propose a deep neural network based on contextual long short-term memory (LSTM) architecture that exploits both content and metadata to detect bots at the tweet level: contextual features are extracted from user metadata and fed as auxiliary input to LSTM deep nets processing the tweet text. Another contribution that we make is proposing a technique based on synthetic minority oversampling to generate a large labeled dataset, suitable for deep nets training, from a minimal amount of labeled data (roughly 3000 examples of sophisticated Twitter bots). We demonstrate that, from just one single tweet, our architecture can achieve high classification accuracy (AUC  >  96%) in separating bots from humans. We apply the same architecture to account-level bot detection, achieving nearly perfect classification accuracy (AUC  >  99%). Our system outperforms previous state of the art while leveraging a small and interpretable set of features, yet requiring minimal training data.

Includes members:
  Member "the rise of jihadist propaganda on social networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "modeling islamist extremist communications on social media using contextual dimensions religion ideology and hate")
  Member "the spread of low credibility content by social bots":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "topology comparison of twitter diffusion networks effectively reveals misleading information")
  Member "ai powered social bots":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "measuring bot and human behavioral dynamics")
  Member "contagion dynamics of extremist propaganda in social networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "predictive situation awareness for ebola virus disease using a collective intelligence multi model integration platform bayes cloud")
  Member "explainable artificial intelligence understanding visualizing and interpreting deep learning models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the convergence of artificial intelligence and distributed ledger technology a scoping review and future research agenda")
  Member "disinformation and social bot operations in the run up to the 2017 french presidential election":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "don t feed the troll detecting troll behavior via inverse reinforcement learning")
  Member "early detection of promoted campaigns on social media":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "don t feed the troll detecting troll behavior via inverse reinforcement learning")
  Member "discovery retrieval and analysis of star wars botnet in twitter":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deception strategies and threats for online discussions")

Question: Please tell me which class the source paper 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.AI(Artificial Intelligence)