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 note on spectral clustering"
  Abstract: Spectral clustering is a popular and successful approach for partitioning the nodes of a graph into clusters for which the ratio of outside connections compared to the volume (sum of degrees) is small. In order to partition into $k$ clusters, one first computes an approximation of the first $k$ eigenvectors of the (normalized) Laplacian of $G$, uses it to embed the vertices of $G$ into $k$-dimensional Euclidean space $\mathbb{R}^k$, and then partitions the resulting points via a $k$-means clustering algorithm. It is an important task for theory to explain the success of spectral clustering. #R##N#Peng et al. (COLT, 2015) made an important step in this direction. They showed that spectral clustering provably works if the gap between the $(k+1)$-th and the $k$-th eigenvalue of the normalized Laplacian is sufficiently large. They prove a structural and an algorithmic result. The algorithmic result needs a considerably stronger gap assumption and does not analyze the standard spectral clustering paradigm; it replaces spectral embedding by heat kernel embedding and $k$-means clustering by locality sensitive hashing. #R##N#We extend their work in two directions. Structurally, we improve the quality guarantee for spectral clustering by a factor of $k$ and simultaneously weaken the gap assumption. Algorithmically, we show that the standard paradigm for spectral clustering works. Moreover, it even works with the same gap assumption as required for the structural result.

Connected through hyperedge HE-1 (induced by source paper "approximating spectral clustering via sampling a review"):
  with members:
    - "optimized deformed laplacian for spectrum based community detection in sparse heterogeneous graphs"
    - "revisiting the bethe hessian improved community detection in sparse heterogeneous graphs"
    - "performance of johnson lindenstrauss transform for k means and k medians clustering"
    - "graph reduction by local variation"
    - "scalable spectral clustering using random binning features"
    - "graph reduction with spectral and cut guarantees"
    - "spectrally approximating large graphs with smaller graphs"
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"

Connected through hyperedge HE-2 (induced by source paper "improved analysis of spectral algorithm for clustering"):
  with members:
    - "convex programming based spectral clustering"
    - "approximate spectral clustering efficiency and guarantees"
    - "partitioning well clustered graphs spectral clustering works"
    - "partitioning into expanders"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-3 (induced by source paper "convex programming based spectral clustering"):
  with members:
    - "emnist an extension of mnist to handwritten letters"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "spectral clustering by ellipsoid and its connection to separable nonnegative matrix factorization"
    - "partitioning well clustered graphs spectral clustering works"
    - "partitioning into expanders"
    - "improved cheeger s inequality analysis of spectral partitioning algorithms through higher order spectral gap"
    - "defining and evaluating network communities based on ground truth"
    - "computing a nonnegative matrix factorization provably"

Connected through hyperedge HE-4 (induced by source paper "partitioning well clustered graphs spectral clustering works"):
  with members:
    - "correlation clustering with noisy partial information"
    - "an almost linear time algorithm for approximate max flow in undirected graphs and its multicommodity generalizations"
    - "partitioning into expanders"
    - "approximation algorithm for sparsest k partitioning"
    - "improved cheeger s inequality analysis of spectral partitioning algorithms through higher order spectral gap"
    - "many sparse cuts via higher eigenvalues"
    - "faster spectral sparsification and numerical algorithms for sdd matrices"
    - "center based clustering under perturbation stability"

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)