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: "a tensor based method for missing traffic data completion"
  Abstract: Missing and suspicious traffic data are inevitable due to detector and communication malfunctions, which adversely affect the transportation management system (TMS). In this paper, a tensor pattern which is an extension of matrix is introduced into modeling the traffic data for the first time, which can give full play to traffic spatial–temporal information and preserve the multi-way nature of traffic data. To estimate the missing value, a tensor decomposition based Imputation method has been developed. This approach not only inherits the advantages of imputation methods based on matrix pattern for estimating missing points, but also well mines the multi-dimensional inherent correlation of traffic data. Experiments demonstrate that the proposed method achieves a better imputation performance than the state-of-the-art imputation approach even when the missing ratio is up to 90%. Furthermore, the experimental results show that the proposed method can address the extreme case where the data of one or several days are completely missing, and additionally it can be employed to recover the missing traffic data in adverse weather as well.

Connected through hyperedge HE-1 (induced by source paper "pca based missing information imputation for real time crash likelihood prediction under imbalanced data"):
  with members:
    - "class imbalance problem in data mining review"
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-2 (induced by source paper "greed is super a fast algorithm for super resolution"):
  with members:
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "music for single snapshot spectral estimation stability and super resolution"
    - "matched filtering from limited frequency samples"
    - "spectral compressive sensing with polar interpolation"
    - "near minimax line spectral estimation"
    - "super resolution via superset selection and pruning"
    - "support detection in super resolution"
    - "super resolution from noisy data"

Connected through hyperedge HE-3 (induced by source paper "exploring algorithmic limits of matrix rank minimization under affine constraints"):
  with members:
    - "generalized nonconvex nonsmooth low rank minimization"
    - "robust recovery of subspace structures by low rank representation"
    - "efficient matrix completion with gaussian models"
    - "tilt transform invariant low rank textures"
    - "guaranteed rank minimization via singular value projection"
    - "robust principal component analysis"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-4 (induced by source paper "ad hoc microphone array calibration euclidean distance matrix completion algorithm and theoretical guarantees"):
  with members:
    - "matrix completion from noisy entries"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-5 (induced by source paper "leveraging side observations in stochastic bandits"):
  with members:
    - "from bandits to experts on the value of side observations"
    - "x armed bandits"
    - "a contextual bandit approach to personalized news article recommendation"
    - "bandit problems with side observations"

Connected through hyperedge HE-6 (induced by source paper "rank aggregation via nuclear norm minimization"):
  with members:
    - "recovering low rank matrices from few coefficients in any basis"
    - "the power of convex relaxation near optimal matrix completion"
    - "set an algorithm for consistent matrix completion"
    - "guaranteed rank minimization via singular value projection"
    - "exact matrix completion via convex optimization"
    - "iterative filtering for a dynamical reputation system"

Connected through hyperedge HE-7 (induced by source paper "large scale convex minimization with a low rank constraint"):
  with members:
    - "matrix completion from a few entries"
    - "a simpler approach to matrix completion"
    - "matrix completion with noise"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-8 (induced by source paper "matrix completion from noisy entries"):
  with members:
    - "collaborative filtering in a non uniform world learning with the weighted trace norm"
    - "robust principal component analysis exact recovery of corrupted low rank matrices"
    - "the power of convex relaxation near optimal matrix completion"
    - "matrix completion from a few entries"
    - "matrix completion with noise"
    - "exact matrix completion via convex optimization"

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.NA(Numerical Analysis)