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 visual interaction framework for dimensionality reduction based data exploration"
  Abstract: Dimensionality reduction is a common method for analyzing and visualizing high-dimensional data. However, reasoning dynamically about the results of a dimensionality reduction is difficult. Dimensionality-reduction algorithms use complex optimizations to reduce the number of dimensions of a dataset, but these new dimensions often lack a clear relation to the initial data dimensions, thus making them difficult to interpret. Here we propose a visual interaction framework to improve dimensionality-reduction based exploratory data analysis. We introduce two interaction techniques, forward projection and backward projection, for dynamically reasoning about dimensionally reduced data. We also contribute two visualization techniques, prolines and feasibility maps, to facilitate the effective use of the proposed interactions. We apply our framework to PCA and autoencoder-based dimensionality reductions. Through data-exploration examples, we demonstrate how our visual interactions can improve the use of dimensionality reduction in exploratory 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"
    - "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"
    - "google s multilingual neural machine translation system enabling zero shot translation"

Connected through hyperedge HE-2 (induced by source paper "semantic concept spaces guided topic model refinement using word embedding projections"):
  with members:
    - "explainer a visual analytics framework for interactive and explainable machine learning"
    - "improving topic models with latent feature word representations"
    - "recent research advances on interactive machine learning"
    - "towards better analysis of machine learning models a visual analytics perspective"
    - "machine teaching a new paradigm for building machine learning systems"
    - "mixing dirichlet topic models and word embeddings to make lda2vec"
    - "conceptnet 5 5 an open multilingual graph of general knowledge"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "conditional t sne complementary t sne embeddings through factoring out prior information"):
  with members:
    - "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"
    - "approximated and user steerable tsne for progressive visual analytics"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-4 (induced by source paper "immersive insights a hybrid analytics system forcollaborative exploratory data analysis"):
  with members:
    - "dataspace a reconfigurable hybrid reality environment for collaborative information analysis"
    - "clustrophile 2 guided visual clustering analysis"
    - "dimreader axis lines that explain non linear projections"
    - "clustrophile a tool for visual clustering analysis"
    - "foresight recommending visual insights"

Connected through hyperedge HE-5 (induced by source paper "dataspace a reconfigurable hybrid reality environment for collaborative information analysis"):
  with members:
    - "clustrophile 2 guided visual clustering analysis"

Connected through hyperedge HE-6 (induced by source paper "dimreader axis lines that explain non linear projections"):
  with members:
    - "vector field k means clustering trajectories by fitting multiple vector fields"
    - "the numpy array a structure for efficient numerical computation"

Connected through hyperedge HE-7 (induced by source paper "dimreader using auto differentiation to explain non linear projections"):
  with members:
    - "theano a python framework for fast computation of mathematical expressions"
    - "the numpy array a structure for efficient numerical computation"

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.HC(Human-Computer Interaction)