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: "clustering with t sne provably"
  Abstract: t-distributed Stochastic Neighborhood Embedding (t-SNE), a clustering and visualization method proposed by van der Maaten & Hinton in 2008, has rapidly become a standard tool in a number of natural sciences. Despite its overwhelming success, there is a distinct lack of mathematical foundations and the inner workings of the algorithm are not well understood. The purpose of this paper is to prove that t-SNE is able to recover well-separated clusters; more precisely, we prove that t-SNE in the `early exaggeration' phase, an optimization technique proposed by van der Maaten & Hinton (2008) and van der Maaten (2014), can be rigorously analyzed. As a byproduct, the proof suggests novel ways for setting the exaggeration parameter $\alpha$ and step size $h$. Numerical examples illustrate the effectiveness of these rules: in particular, the quality of embedding of topological structures (e.g. the swiss roll) improves. We also discuss a connection to spectral clustering methods.

Connected through hyperedge HE-1 (induced by source paper "t visne interactive assessment and interpretation of t sne projections"):
  with members:
    - "supporting analysis of dimensionality reduction results with contrastive learning"
    - "fast interpolation based t sne for improved visualization of single cell rna seq data"
    - "clustrophile 2 guided visual clustering analysis"
    - "rulematrix visualizing and understanding classifiers with rules"
    - "a visual interaction framework for dimensionality reduction based data exploration"
    - "dimreader axis lines that explain non linear projections"
    - "t sne cuda gpu accelerated t sne and its applications to modern data"
    - "google s multilingual neural machine translation system enabling zero shot translation"

Connected through hyperedge HE-2 (induced by source paper "tree sne hierarchical clustering and visualization using t sne"):
  with members:
    - "fast interpolation based t sne for improved visualization of single cell rna seq data"
    - "heavy tailed kernels reveal a finer cluster structure in t sne visualisations"
    - "deep clustering via joint convolutional autoencoder embedding and relative entropy minimization"
    - "joint unsupervised learning of deep representations and image clusters"
    - "learning a task specific deep architecture for clustering"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-3 (induced by source paper "graspel graph spectral learning at scale"):
  with members:
    - "learning graphs from data a signal representation perspective"
    - "nearly linear time spectral graph reduction for scalable graph partitioning and data visualization"
    - "similarity aware spectral sparsification by edge filtering"
    - "towards scalable spectral clustering via spectrum preserving sparsification"
    - "graph learning from data under laplacian and structural constraints"
    - "partitioning well clustered graphs spectral clustering works"
    - "learning laplacian matrix in smooth graph signal representations"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-4 (induced by source paper "improving stochastic neighbour embedding fundamentally with a well defined data dependent kernel"):
  with members:
    - "nearest neighbour induced isolation similarity and its impact on density based clustering"
    - "fast interpolation based t sne for improved visualization of single cell rna seq data"
    - "an analysis of the t sne algorithm for data visualization"
    - "feature selection a data perspective"
    - "approximated and user steerable tsne for progressive visual analytics"

Connected through hyperedge HE-5 (induced by source paper "fast interpolation based t sne for improved visualization of single cell rna seq data"):
  with members:
    - "randomized algorithms for low rank matrix factorizations sharp performance bounds"

Connected through hyperedge HE-6 (induced by source paper "nearly linear time spectral graph reduction for scalable graph partitioning and data visualization"):
  with members:
    - "spectrally approximating large graphs with smaller graphs"
    - "almost linear time algorithms for markov chains and new spectral primitives for directed graphs"
    - "approximate spectral clustering efficiency and guarantees"
    - "partitioning well clustered graphs spectral clustering works"
    - "recent advances in graph partitioning"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "community detection and graph partitioning"
    - "electrical flows laplacian systems and faster approximation of maximum flow in undirected graphs"

Connected through hyperedge HE-7 (induced by source paper "an analysis of the t sne algorithm for data visualization"):
  with members:
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "better agnostic clustering via relaxed tensor norms"
    - "list decodable robust mean estimation and learning mixtures of spherical gaussians"
    - "mixture models robustness and sum of squares proofs"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "matrix completion has no spurious local minimum"
    - "learning mixtures of gaussians in high dimensions"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"

Connected through hyperedge HE-8 (induced by source paper "bigmap big data mapping with parallelized t sne"):
  with members:
    - "fast interpolation based t sne for improved visualization of single cell rna seq data"
    - "an analysis of the t sne algorithm for data visualization"
    - "t sne cuda gpu accelerated t sne and its applications to modern data"
    - "why are big data matrices approximately low rank"
    - "approximated and user steerable tsne for progressive visual analytics"

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.LG(Machine Learning)