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: "t sne cuda gpu accelerated t sne and its applications to modern data"
  Abstract: Modern datasets and models are notoriously difficult to explore and analyze due to their inherent high dimensionality and massive numbers of samples. Existing visualization methods which employ dimensionality reduction to two or three dimensions are often inefficient and/or ineffective for these datasets. This paper introduces t-SNE-CUDA, a GPU-accelerated implementation of t-distributed Symmetric Neighbor Embedding (t-SNE) for visualizing datasets and models. t-SNE-CUDA significantly outperforms current implementations with 50-700x speedups on the CIFAR-10 and MNIST datasets. These speedups enable, for the first time, visualization of the neural network activations on the entire ImageNet dataset - a feat that was previously computationally intractable. We also demonstrate visualization performance in the NLP domain by visualizing the GloVe embedding vectors. From these visualizations, we can draw interesting conclusions about using the L2 metric in these embedding spaces. t-SNE-CUDA is publicly available atthis https URL

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"
    - "clustering with t sne provably"
    - "google s multilingual neural machine translation system enabling zero shot translation"

Connected through hyperedge HE-2 (induced by source paper "gpgpu linear complexity t sne optimization"):
  with members:
    - "progressive data science potential and challenges"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "billion scale similarity search with gpus"
    - "pixelsne visualizing fast with just enough precision via pixel aligned stochastic neighbor embedding"
    - "progressive analytics a computation paradigm for exploratory data analysis"
    - "going deeper with convolutions"

Connected through hyperedge HE-3 (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"
    - "why are big data matrices approximately low rank"
    - "clustering with t sne provably"
    - "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)