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 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.

Connected through hyperedge HE-1 (induced by source paper "a survey on big data for network traffic monitoring and analysis"):
  with members:
    - "data analytics service composition and deployment on edge devices"
    - "net2vec deep learning for the network"
    - "in a world that counts clustering and detecting fake social engagement at scale"
    - "a new approach to dimensionality reduction for anomaly detection in data traffic"
    - "efficacy of live ddos detection with hadoop"
    - "the family of mapreduce and large scale data processing systems"
    - "building high level features using large scale unsupervised learning"

Connected through hyperedge HE-2 (induced by source paper "fast online low rank tensor subspace tracking by cp decomposition using recursive least squares from incomplete observations"):
  with members:
    - "inductive framework for multi aspect streaming tensor completion with side information"
    - "online low rank tensor subspace tracking from incomplete data by cp decomposition using recursive least squares"
    - "low rank tensor completion a riemannian manifold preconditioning approach"
    - "bayesian robust tensor factorization for incomplete multiway data"
    - "bayesian cp factorization of incomplete tensors with automatic rank determination"
    - "res regularized stochastic bfgs algorithm"
    - "rank regularization and bayesian inference for tensor completion and extrapolation"
    - "dynamic anomalography tracking network anomalies via sparsity and low rank"

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.NI(Networking and Internet Architecture)