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 "network volume anomaly detection and identification in large scale networks based on online time structured traffic tensor tracking"):
  Abstract: This paper addresses network anomography, that is, the problem of inferring network-level anomalies from indirect link measurements. This problem is cast as a low-rank subspace tracking problem for normal flows under incomplete observations and an outlier detection problem for abnormal flows. Since traffic data is large-scale time-structured data accompanied with noise and outliers under partial observations, an efficient modeling method is essential. To this end, this paper proposes an online subspace tracking of a Hankelized time-structured traffic tensor for normal flows based on the Candecomp/PARAFAC decomposition exploiting the recursive least squares algorithm. We estimate abnormal flows as outlier sparse flows via sparsity maximization in the underlying under-constrained linear-inverse problem. A major advantage is that our algorithm estimates normal flows by low-dimensional matrices with time-directional features as well as the spatial correlation of multiple links without using the past observed measurements and the past model parameters. Extensive numerical evaluations show that the proposed algorithm achieves faster convergence per iteration of model approximation and better volume anomaly detection performance compared to state-of-the-art algorithms.

Includes members:
  Member "online low rank tensor subspace tracking from incomplete data by cp decomposition using recursive least squares":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "low rank tensor grid for image completion")
  Member "network traffic decomposition for anomaly detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deephttp semantics structure model with attention for anomalous http traffic detection and pattern mining")
  Member "estimating traffic and anomaly maps via network tomography":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ddos attacks with randomized traffic innovation botnet identification challenges and strategies")
  Member "rank regularization and bayesian inference for tensor completion and extrapolation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "coupled tensor completion via low rank tensor ring")
  Member "prost a smoothed lp norm robust online subspace tracking method for realtime background subtraction in video":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "static and dynamic robust pca and matrix completion a review")
  Member "dynamic anomalography tracking network anomalies via sparsity and low rank":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "static and dynamic robust pca and matrix completion a review")
  Member "structural analysis of network traffic matrix via relaxed principal component pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "an improved traffic matrix decomposition method with frequency domain regularization")
  Member "online identification and tracking of subspaces from highly incomplete information":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "online high rank matrix completion")

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.NI(Networking and Internet Architecture)