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: "localization on low order eigenvectors of data matrices"
  Abstract: Eigenvector localization refers to the situation when most of the components of an eigenvector are zero or near-zero. This phenomenon has been observed on eigenvectors associated with extremal eigenvalues, and in many of those cases it can be meaningfully interpreted in terms of "structural heterogeneities" in the data. For example, the largest eigenvectors of adjacency matrices of large complex networks often have most of their mass localized on high-degree nodes; and the smallest eigenvectors of the Laplacians of such networks are often localized on small but meaningful community-like sets of nodes. Here, we describe localization associated with low-order eigenvectors, i.e., eigenvectors corresponding to eigenvalues that are not extremal but that are "buried" further down in the spectrum. Although we have observed it in several unrelated applications, this phenomenon of low-order eigenvector localization defies common intuitions and simple explanations, and it creates serious difficulties for the applicability of popular eigenvector-based machine learning and data analysis tools. After describing two examples where low-order eigenvector localization arises, we present a very simple model that qualitatively reproduces several of the empirically-observed results. This model suggests certain coarse structural similarities among the seemingly-unrelated applications where we have observed low-order eigenvector localization, and it may be used as a diagnostic tool to help extract insight from data graphs when such low-order eigenvector localization is present.

Connected through hyperedge HE-1 (induced by source paper "spectra of networks containing short loops"):
  with members:
    - "localization and centrality in networks"
    - "graph spectra and the detectability of community structure in networks"

Connected through hyperedge HE-2 (induced by source paper "spectral embedding norm looking deep into the spectrum of the graph laplacian"):
  with members:
    - "image anomalies a review and synthesis of detection methods"
    - "salient object detection a benchmark"
    - "graph based anomaly detection and description a survey"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-3 (induced by source paper "signal recovery on graphs fundamental limits of sampling strategies"):
  with members:
    - "reconstruction of graph signals through percolation from seeding nodes"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "subgraph based filterbanks for graph signals"
    - "compressive spectral clustering"
    - "sampling of graph signals with successive local aggregations"
    - "signals on graphs uncertainty principle and sampling"
    - "signal recovery on graphs random versus experimentally designed sampling"
    - "signal representations on graphs tools and applications"

Connected through hyperedge HE-4 (induced by source paper "signal representations on graphs tools and applications"):
  with members:
    - "reconstruction of graph signals through percolation from seeding nodes"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "constrained adaptive sensing"
    - "signals on graphs uncertainty principle and sampling"
    - "signal recovery on graphs random versus experimentally designed sampling"
    - "local set based graph signal reconstruction"
    - "discrete signal processing on graphs sampling theory"
    - "signal recovery on graphs fundamental limits of sampling strategies"

Connected through hyperedge HE-5 (induced by source paper "synchronization over z2 and community detection in signed multiplex networks with constraints"):
  with members:
    - "eigenvector synchronization graph rigidity and the molecule problem"
    - "extracting spatial information from networks with low order eigenvectors"
    - "efficient and principled method for detecting communities in networks"

Connected through hyperedge HE-6 (induced by source paper "spectral partitioning with blends of eigenvectors"):
  with members:
    - "recent advances in graph partitioning"
    - "spectral clustering via the power method provably"

Connected through hyperedge HE-7 (induced by source paper "revisiting the nystrom method for improved large scale machine learning"):
  with members:
    - "matrix coherence and the nystrom method"
    - "improving cur matrix decomposition and the nystr o m approximation via adaptive sampling"
    - "improved bounds for the nystrom method with application to kernel classification"
    - "improved matrix algorithms via the subsampled randomized hadamard transform"
    - "sharp analysis of low rank kernel matrix approximations"
    - "stochastic low rank kernel learning for regression"
    - "fast approximation of matrix coherence and statistical leverage"
    - "lsrn a parallel iterative solver for strongly over or under determined systems"

Connected through hyperedge HE-8 (induced by source paper "semi supervised eigenvectors for large scale locally biased learning"):
  with members:
    - "matrix coherence and the nystrom method"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "regularized laplacian estimation and fast eigenvector approximation"
    - "the push algorithm for spectral ranking"
    - "implementing regularization implicitly via approximate eigenvector computation"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"
    - "a local spectral method for graphs with applications to improving graph partitions and exploring data graphs locally"
    - "nearly linear time algorithms for graph partitioning graph sparsification and solving linear systems"

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.DM(Discrete Mathematics)