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: "approximated and user steerable tsne for progressive visual analytics"
  Abstract: Progressive Visual Analytics aims at improving the interactivity in existing analytics techniques by means of visualization as well as interaction with intermediate results. One key method for data analysis is dimensionality reduction, for example, to produce 2D embeddings that can be visualized and analyzed efficiently. t-Distributed Stochastic Neighbor Embedding (tSNE) is a well-suited technique for the visualization of several high-dimensional data. tSNE can create meaningful intermediate results but suffers from a slow initialization that constrains its application in Progressive Visual Analytics. We introduce a controllable tSNE approximation (A-tSNE), which trades off speed and accuracy, to enable interactive data exploration. We offer real-time visualization techniques, including a density-based solution and a Magic Lens to inspect the degree of approximation. With this feedback, the user can decide on local refinements and steer the approximation level during the analysis. We demonstrate our technique with several datasets, in a real-world research scenario and for the real-time analysis of high-dimensional streams to illustrate its effectiveness for interactive data analysis.

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"
    - "clustering with t sne provably"

Connected through hyperedge HE-2 (induced by source paper "an incremental dimensionality reduction method for visualizing streaming multidimensional data"):
  with members:
    - "progressive data science potential and challenges"
    - "online visual analytics of text streams"
    - "an overview of gradient descent optimization algorithms"
    - "adadelta an adaptive learning rate method"
    - "gmap drawing graphs as maps"

Connected through hyperedge HE-3 (induced by source paper "a visual analytics framework for reviewing streaming performance data"):
  with members:
    - "an incremental dimensionality reduction method for visualizing streaming multidimensional data"
    - "progressive data science potential and challenges"

Connected through hyperedge HE-4 (induced by source paper "spaceland embedding of sparse stochastic graphs"):
  with members:
    - "fast interpolation based t sne for improved visualization of single cell rna seq data"
    - "geometric deep learning going beyond euclidean data"
    - "line large scale information network embedding"
    - "imagenet large scale visual recognition challenge"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "distributed representations of words and phrases and their compositionality"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-5 (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"
    - "visualizing large scale and high dimensional data"
    - "censoring representations with an adversary"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-6 (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"
    - "clustering with t sne provably"

Connected through hyperedge HE-7 (induced by source paper "interactive dimensionality reduction using similarity projections"):
  with members:
    - "dimensionality reduction using similarity induced embeddings"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "progressive data science potential and challenges"):
  with members:
    - "a survey on deep learning toolkits and libraries for intelligent user interfaces"
    - "helix holistic optimization for accelerating iterative machine learning"
    - "progressive neural networks"
    - "generative adversarial active learning"
    - "enabling robots to communicate their objectives"
    - "data polygamy the many many relationships among urban spatio temporal data sets"
    - "progressive analytics a computation paradigm for exploratory data analysis"
    - "revisiting reuse in main memory database 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.CV(Computer Vision and Pattern Recognition)