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: "streaming kernel principal component analysis"
  Abstract: Kernel principal component analysis (KPCA) provides a concise set of basis vectors which capture non-linear structures within large data sets, and is a central tool in data analysis and learning. To allow for non-linear relations, typically a full $n \times n$ kernel matrix is constructed over $n$ data points, but this requires too much space and time for large values of $n$. Techniques such as the Nystr\"om method and random feature maps can help towards this goal, but they do not explicitly maintain the basis vectors in a stream and take more space than desired. We propose a new approach for streaming KPCA which maintains a small set of basis elements in a stream, requiring space only logarithmic in $n$, and also improves the dependence on the error parameter. Our technique combines together random feature maps with recent advances in matrix sketching, it has guaranteed spectral norm error bounds with respect to the original kernel matrix, and it compares favorably in practice to state-of-the-art approaches.

Connected through hyperedge HE-1 (induced by source paper "the gaussiansketch for almost relative error kernel distance"):
  with members:
    - "hashing based estimators for kernel density in high dimensions"
    - "random fourier features for kernel ridge regression approximation bounds and statistical guarantees"
    - "locality sensitive hashing of curves"
    - "sublinear time low rank approximation of positive semidefinite matrices"
    - "is input sparsity time possible for kernel low rank approximation"
    - "optimality of the johnson lindenstrauss lemma"
    - "fastmmd ensemble of circular discrepancy for efficient two sample test"
    - "sketching as a tool for numerical linear algebra"

Connected through hyperedge HE-2 (induced by source paper "streaming kernel pca with tilde o sqrt n random features"):
  with members:
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "lazysvd even faster svd decomposition yet without agonizing pain"
    - "scale up nonlinear component analysis with doubly stochastic gradients"
    - "simple and deterministic matrix sketching"

Connected through hyperedge HE-3 (induced by source paper "empirical evaluation of kernel pca approximation methods in classification tasks"):
  with members:
    - "kernel principal component analysis and its applications in face recognition and active shape models"
    - "simple and deterministic matrix sketching"

Connected through hyperedge HE-4 (induced by source paper "recursive sampling for the nystr om method"):
  with members:
    - "relative error embeddings for the gaussian kernel distance"
    - "fast dpp sampling for nystr om with application to kernel methods"
    - "on column selection in approximate kernel canonical correlation analysis"
    - "large scale kernel learning using block coordinate descent"
    - "ridge leverage scores for low rank approximation"
    - "input sparsity time low rank approximation via ridge leverage score sampling"
    - "frequent directions simple and deterministic matrix sketching"
    - "optimal principal component analysis in distributed and streaming models"

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)