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: "optimal algorithms for l_ 1 subspace signal processing"
  Abstract: We describe ways to define and calculate L#N# 1#N#-norm signal subspaces that are less sensitive to outlying data than L#N# 2#N#-calculated subspaces. We start with the computation of the L#N# 1#N# maximum-projection principal component of a data matrix containing N signal samples of dimension D. We show that while the general problem is formally NP-hard in asymptotically large N, D, the case of engineering interest of fixed dimension D and asymptotically large sample size N is not. In particular, for the case where the sample size is less than the fixed dimension , we present in explicit form an optimal algorithm of computational cost 2#N# N#N#. For the case N ≥ D, we present an optimal algorithm of complexity O(N#N# D#N#). We generalize to multiple L#N# 1#N#-max-projection components and present an explicit optimal L#N# 1#N# subspace calculation algorithm of complexity O(N#N# DK-K+1#N#) where K is the desired number of L#N# 1#N# principal components (subspace rank). We conclude with illustrations of L#N# 1#N#-subspace signal processing in the fields of data dimensionality reduction, direction-of-arrival estimation, and image conditioning/restoration.

Connected through hyperedge HE-1 (induced by source paper "robust subspace recovery with adversarial outliers"):
  with members:
    - "an overview of robust subspace recovery"
    - "a well tempered landscape for non convex robust subspace recovery"
    - "provable self representation based outlier detection in a union of subspaces"
    - "resilience a criterion for learning in the presence of arbitrary outliers"
    - "input sparsity and hardness for robust subspace approximation"
    - "robust computation of linear models by convex relaxation"
    - "algorithms and hardness for robust subspace recovery"
    - "robust pca via outlier pursuit"

Connected through hyperedge HE-2 (induced by source paper "l1 norm tucker tensor decomposition"):
  with members:
    - "l1 norm principal component analysis of complex data"
    - "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"
    - "robust pca via outlier pursuit"

Connected through hyperedge HE-3 (induced by source paper "a generalization of principal component analysis"):
  with members:
    - "a tutorial on principal component analysis"

Connected through hyperedge HE-4 (induced by source paper "an overview of robust subspace recovery"):
  with members:
    - "fast robust and non convex subspace recovery"
    - "static and dynamic robust pca via low rank sparse matrix decomposition a review"
    - "on the complexity of robust pca and l1 norm low rank matrix approximation"
    - "estimation of camera locations in highly corrupted scenarios all about that base no shape trouble"
    - "static and dynamic robust pca and matrix completion a review"
    - "being robust in high dimensions can be practical"
    - "a new rank constraint on multi view fundamental matrices and its application to camera location recovery"
    - "a well tempered landscape for non convex robust subspace recovery"

Connected through hyperedge HE-5 (induced by source paper "l1 norm principal component analysis of complex data"):
  with members:
    - "designing unimodular codes via quadratic optimization"

Connected through hyperedge HE-6 (induced by source paper "the exact solution to rank 1 l1 norm tucker2 decomposition"):
  with members:
    - "efficient l1 norm principal component analysis via bit flipping"
    - "robust principal component analysis"

Connected through hyperedge HE-7 (induced by source paper "efficient l1 norm principal component analysis via bit flipping"):
  with members:
    - "robust principal component analysis"

Connected through hyperedge HE-8 (induced by source paper "correlation clustering with low rank matrices"):
  with members:
    - "the ground truth about metadata and community detection in networks"
    - "node2vec scalable feature learning for networks"
    - "bipartite correlation clustering maximizing agreements"
    - "deepwalk online learning of social representations"
    - "the sparse principal component of a constant rank matrix"
    - "social structure of facebook networks"

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)