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: "l1 norm principal component analysis of complex data"
  Abstract: L1-norm Principal-Component Analysis (L1-PCA) of real-valued data has attracted significant research interest over the past decade. L1-PCA of complex-valued data remains to date unexplored despite the many possible applications (in communication systems, for example). In this paper, we establish theoretical and algorithmic foundations of L1-PCA of complex-valued data matrices. Specifically, we first show that, in contrast to the real-valued case for which an optimal polynomial-cost algorithm was recently reported by Markopoulos, Karystinos, and Pados, complex L1-PCA is formally NP-hard. Then, casting complex L1-PCA as a unimodular optimization problem, we present the first two suboptimal algorithms in the literature for its solution. Extensive experimental studies included in this paper illustrate the sturdy resistance of complex L1-PCA against faulty measurements/outliers in the processed data.

Connected through hyperedge HE-1 (induced by source paper "l1 norm tucker tensor decomposition"):
  with members:
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "the exact solution to rank 1 l1 norm tucker2 decomposition"
    - "efficient l1 norm principal component analysis via bit flipping"
    - "classification via tensor decompositions of echo state networks"
    - "parallel tensor compression for large scale scientific data"
    - "optimal algorithms for l_ 1 subspace signal processing"
    - "robust pca via outlier pursuit"

Connected through hyperedge HE-2 (induced by source paper "nonlinear dimensionality reduction for discriminative analytics of multiple datasets"):
  with members:
    - "canonical correlation analysis of datasets with a common source graph"
    - "dpca dimensionality reduction for discriminative analytics of multiple large scale datasets"
    - "fast robust pca on graphs"

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.DS(Data Structures and Algorithms)