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: "network traffic decomposition for anomaly detection"
  Abstract: In this paper we focus on the detection of network anomalies like Denial of Service (DoS) attacks and port scans in a unified manner. While there has been an extensive amount of research in network anomaly detection, current state of the art methods are only able to detect one class of anomalies at the cost of others. The key tool we will use is based on the spectral decomposition of a trajectory/hankel matrix which is able to detect deviations from both between and within correlation present in the observed network traffic data. Detailed experiments on synthetic and real network traces shows a significant improvement in detection capability over competing approaches. In the process we also address the issue of robustness of anomaly detection systems in a principled fashion.

Connected through hyperedge HE-1 (induced by source paper "deephttp semantics structure model with attention for anomalous http traffic detection and pattern mining"):
  with members:
    - "network traffic anomaly detection using recurrent neural networks"
    - "dipole diagnosis prediction in healthcare via attention based bidirectional recurrent neural networks"
    - "effective approaches to attention based neural machine translation"
    - "show attend and tell neural image caption generation with visual attention"
    - "attention based models for speech recognition"
    - "constructing long short term memory based deep recurrent neural networks for large vocabulary speech recognition"

Connected through hyperedge HE-2 (induced by source paper "network volume anomaly detection and identification in large scale networks based on online time structured traffic tensor tracking"):
  with members:
    - "online low rank tensor subspace tracking from incomplete data by cp decomposition using recursive least squares"
    - "estimating traffic and anomaly maps via network tomography"
    - "rank regularization and bayesian inference for tensor completion and extrapolation"
    - "prost a smoothed lp norm robust online subspace tracking method for realtime background subtraction in video"
    - "dynamic anomalography tracking network anomalies via sparsity and low rank"
    - "structural analysis of network traffic matrix via relaxed principal component pursuit"
    - "online identification and tracking of subspaces from highly incomplete information"
    - "robust principal component analysis"

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)