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: "wikipedia vandal early detection from user behavior to user embedding"
  Abstract: Wikipedia is the largest online encyclopedia that allows anyone to edit articles. In this paper, we propose the use of deep learning to detect vandals based on their edit history. In particular, we develop a multi-source long-short term memory network (M-LSTM) to model user behaviors by using a variety of user edit aspects as inputs, including the history of edit reversion information, edit page titles and categories. With M-LSTM, we can encode each user into a low dimensional real vector, called user embedding. Meanwhile, as a sequential model, M-LSTM updates the user embedding each time after the user commits a new edit. Thus, we can predict whether a user is benign or vandal dynamically based on the up-to-date user embedding. Furthermore, those user embeddings are crucial to discover collaborative vandals.

Connected through hyperedge HE-1 (induced by source paper "stre self attentive edit quality prediction in wikipedia"):
  with members:
    - "neural based statement classification for biased language"
    - "universal language model fine tuning for text classification"
    - "distilling information reliability and source trustworthiness from digital traces"
    - "a structured self attentive sentence embedding"
    - "building automated vandalism detection tools for wikidata"
    - "empath understanding topic signals in large scale text"
    - "end to end memory networks"
    - "learning transferable features with deep adaptation networks"

Connected through hyperedge HE-2 (induced by source paper "one class adversarial nets for fraud detection"):
  with members:
    - "false information on web and social media a survey"
    - "good semi supervised learning that requires a bad gan"
    - "neural machine translation and sequence to sequence models a tutorial"
    - "an army of me sockpuppets in online discussion communities"
    - "spectrum based deep neural networks for fraud detection"
    - "anyone can become a troll causes of trolling behavior in online discussions"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "energy based generative adversarial network"

Connected through hyperedge HE-3 (induced by source paper "safe a neural survival analysis model for fraud early detection"):
  with members:
    - "a recurrent neural network survival model predicting web user return time"
    - "false information on web and social media a survey"
    - "deep learning for patient specific kidney graft survival analysis"
    - "machine learning for survival analysis a survey"
    - "spectrum based deep neural networks for fraud detection"
    - "vews a wikipedia vandal early warning system"
    - "adam a method for stochastic optimization"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

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)