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: "recurrent neural network attention mechanisms for interpretable system log anomaly detection"
  Abstract: Deep learning has recently demonstrated state-of-the art performance on key tasks related to the maintenance of computer systems, such as intrusion detection, denial of service attack detection, hardware and software system failures, and malware detection. In these contexts, model interpretability is vital for administrator and analyst to trust and act on the automated analysis of machine learning models. Deep learning methods have been criticized as black box oracles which allow limited insight into decision factors. In this work we seek to "bridge the gap" between the impressive performance of deep learning models and the need for interpretable model introspection. To this end we present recurrent neural network (RNN) language models augmented with attention for anomaly detection in system logs. Our methods are generally applicable to any computer system and logging source. #R##N#By incorporating attention variants into our RNN language models we create opportunities for model introspection and analysis without sacrificing state-of-the art performance. #R##N#We demonstrate model performance and illustrate model interpretability on an intrusion detection task using the Los Alamos National Laboratory (LANL) cyber security dataset, reporting upward of 0.99 area under the receiver operator characteristic curve despite being trained only on a single day's worth of data.

Connected through hyperedge HE-1 (induced by source paper "using deep learning to solve computer security challenges a survey"):
  with members:
    - "deepcheck a non intrusive control flow integrity checking based on deep learning"
    - "convolutional neural networks over control flow graphs for software defect prediction"
    - "deep reinforcement fuzzing"
    - "neuzz efficient fuzzing with neural program smoothing"
    - "ropnn detection of rop payloads using deep neural networks"
    - "neural network based graph embedding for cross platform binary code similarity detection"
    - "not all bytes are equal neural byte sieve for fuzzing"
    - "generic black box end to end attack against state of the art api call based malware classifiers"

Connected through hyperedge HE-2 (induced by source paper "finding rats in cats detecting stealthy attacks using group anomaly detection"):
  with members:
    - "an overview of deep learning based methods for unsupervised and semi supervised anomaly detection in videos"
    - "enabling trust in deep learning models a digital forensics case study"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "adam a method for stochastic optimization"
    - "graph based anomaly detection and description a survey"

Connected through hyperedge HE-3 (induced by source paper "attention models in graphs a survey"):
  with members:
    - "deeply learning molecular structure property relationships using attention and gate augmented graph convolutional network"
    - "treegan syntax aware sequence generation with generative adversarial networks"
    - "graph2seq graph to sequence learning with attention based neural networks"
    - "spine structural identity preserved inductive network embedding"
    - "an attention based collaboration framework for multi view network representation learning"
    - "on sampling from massive graph streams"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "dipole diagnosis prediction in healthcare via attention based bidirectional recurrent neural networks"

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.LG(Machine Learning)