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 "recurrent neural network based user authentication for freely typed keystroke data"):
  Abstract: Keystroke dynamics-based user authentication (KDA) based on long and freely typed text is an enhanced user authentication method that can not only identify the validity of current users during login but also continuously monitors the consistency of typing behavior after the login process. Previous long and freely typed text-based KDA methods had difficulty incorporating the key sequence information and handling variable lengths of keystrokes, which in turn resulted in lower authentication performance compared to KDA methods based on short and fixed-length text. To overcome these limitations, we propose a recurrent neural network (RNN)-based KDA model. As the RNN model can process an arbitrary length of input and target sequences, our proposed model takes two consecutive keys as the input sequence and actual typing time for the corresponding key sequence as the target sequence. Based on experimental results involving 120 participants, our proposed RNN-KDA model yielded the best authentication performance for all training and test length combinations in terms of equal error rate (EER). It achieved a 5%-6% EER using only 10 test keystrokes while the EERs of other benchmark methods were above 20%. In addition, its performance steadily and more rapidly improves compared to the benchmark methods when the length of training keystrokes increases.

Includes members:
  Member "lstm a search space odyssey":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "refined gate a simple and effective gating mechanism for recurrent units")
  Member "which encoding is the best for text classification in chinese english japanese and korean":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "glyce glyph vectors for chinese character representations")
  Member "recurrent neural network for text classification with multi task learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "meta embeddings based on self attention")

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.CR(Cryptography and Security)