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: "intrusion prediction with system call sequence to sequence model"
  Abstract: The advanced development of the Internet facilitates efficient information exchange while also been exploited by adversaries. Intrusion detection system (IDS) as an important defense component of network security has always been widely studied in security research. However, research on intrusion prediction, which is more critical for network security, is received less attention. We argue that the advanced anticipation and timely impede of invasion is more vital than simple alarms in security defenses. General research methods regarding prediction are analyzing short term of system-calls to predict forthcoming abnormal behaviors. In this paper we take advantages of the remarkable performance of recurrent neural networks (RNNs) in dealing with long sequential problem, introducing the sequence-to-sequence model into our intrusion prediction work. By semantic modeling system-calls we build a robust system-call sequence-to-sequence prediction model. With taking the system-call traces invoked during the program running as known prerequisite, our model predicts sequence of system-calls that is most likely to be executed in a near future period of time that enabled the ability of monitoring system status and prophesying the intrusion behaviors. Our experiments show that the predict method proposed in this paper achieved well prediction performance on ADFALD intrusion detection test data set. Moreover, the predicted sequence, combined with the known invoked traces of system, significantly improves the performance of intrusion detection verified on various classifiers.

Connected through hyperedge HE-1 (induced by source paper "weighted automata extraction from recurrent neural networks via regression on state spaces"):
  with members:
    - "state regularized recurrent neural networks"
    - "explaining black boxes on sequential data using weighted automata"
    - "on the practical computational power of finite precision rnns for language recognition"
    - "connecting weighted automata and recurrent neural networks through spectral learning"
    - "bisimulation metrics for weighted automata"
    - "extracting automata from recurrent neural networks using queries and counterexamples"
    - "crafting adversarial input sequences for recurrent neural networks"
    - "advances in all neural speech recognition"

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)