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: "visualizing large scale and high dimensional data"
  Abstract: We study the problem of visualizing large-scale and high-dimensional data in a low-dimensional (typically 2D or 3D) space. Much success has been reported recently by techniques that first compute a similarity structure of the data points and then project them into a low-dimensional space with the structure preserved. These two steps suffer from considerable computational costs, preventing the state-of-the-art methods such as the t-SNE from scaling to large-scale and high-dimensional data (e.g., millions of data points and hundreds of dimensions). We propose the LargeVis, a technique that first constructs an accurately approximated K-nearest neighbor graph from the data and then layouts the graph in the low-dimensional space. Comparing to t-SNE, LargeVis significantly reduces the computational cost of the graph construction step and employs a principled probabilistic model for the visualization step, the objective of which can be effectively optimized through asynchronous stochastic gradient descent with a linear time complexity. The whole procedure thus easily scales to millions of high-dimensional data points. Experimental results on real-world data sets demonstrate that the LargeVis outperforms the state-of-the-art methods in both efficiency and effectiveness. The hyper-parameters of LargeVis are also much more stable over different data sets.

Connected through hyperedge HE-1 (induced by source paper "graphvite a high performance cpu gpu hybrid system for node embedding"):
  with members:
    - "bmf block matrix approach to factorization of large scale data"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "verse versatile graph embeddings from similarity measures"
    - "representation learning on graphs methods and applications"
    - "neural message passing for quantum chemistry"
    - "mixed precision training"
    - "parallelizing word2vec in multi core and many core architectures"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"

Connected through hyperedge HE-2 (induced by source paper "conditional t sne complementary t sne embeddings through factoring out prior information"):
  with members:
    - "a visual interaction framework for dimensionality reduction based data exploration"
    - "learning adversarially fair and transferable representations"
    - "dimreader axis lines that explain non linear projections"
    - "node2vec scalable feature learning for networks"
    - "censoring representations with an adversary"
    - "approximated and user steerable tsne for progressive visual analytics"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-3 (induced by source paper "2 d embedding of large and high dimensional data with minimal memory and computational time requirements"):
  with members:
    - "fast interpolation based t sne for improved visualization of single cell rna seq data"
    - "compressive embedding and visualization using graphs"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-4 (induced by source paper "improving the projection of global structures in data through spanning trees"):
  with members:
    - "understanding deep neural networks using topological data analysis"
    - "on the persistence of clustering solutions and true number of clusters in a dataset"
    - "approximation of distances and shortest paths in the broadcast congest clique"

Connected through hyperedge HE-5 (induced by source paper "heavy tailed kernels reveal a finer cluster structure in t sne visualisations"):
  with members:
    - "fast interpolation based t sne for improved visualization of single cell rna seq data"
    - "stochastic neighbor embedding under f divergences"

Connected through hyperedge HE-6 (induced by source paper "deep co space sample mining across feature transformation for semi supervised learning"):
  with members:
    - "multi view intact space learning"
    - "active self paced learning for cost effective and progressive face identification"
    - "energy based generative adversarial network"
    - "deep structured scene parsing by learning with image descriptions"
    - "semi supervised classification with graph convolutional networks"
    - "revisiting semi supervised learning with graph embeddings"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "multi view convolutional neural networks for 3d shape recognition"

Connected through hyperedge HE-7 (induced by source paper "multi level network embedding with boosted low rank matrix approximation"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "modeling polypharmacy side effects with graph convolutional networks"
    - "representation learning on graphs methods and applications"
    - "a survey on network embedding"
    - "name disambiguation in anonymized graphs using network embedding"
    - "attributed network embedding for learning in a dynamic environment"
    - "node2vec scalable feature learning for networks"
    - "pte predictive text embedding through large scale heterogeneous text networks"

Connected through hyperedge HE-8 (induced by source paper "mqapviz a divide and conquer multi objective optimization algorithm to compute large data visualizations"):
  with members:
    - "pasmoqap a parallel asynchronous memetic algorithm for solving the multi objective quadratic assignment problem"
    - "node2vec scalable feature learning for networks"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "distributed representations of words and phrases and their compositionality"

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)