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: "dimreader axis lines that explain non linear projections"
  Abstract: Non-linear dimensionality reduction (NDR) methods such as LLE and t-SNE are popular with visualization researchers and experienced data analysts, but present serious problems of interpretation. In this paper, we present DimReader, a technique that recovers readable axes from such techniques. DimReader is based on analyzing infinitesimal perturbations of the dataset with respect to variables of interest. The perturbations define exactly how we want to change each point in the original dataset and we measure the effect that these changes have on the projection. The recovered axes are in direct analogy with the axis lines (grid lines) of traditional scatterplots. We also present methods for discovering perturbations on the input data that change the projection the most. The calculation of the perturbations is efficient and easily integrated into programs written in modern programming languages. We present results of DimReader on a variety of NDR methods and datasets both synthetic and real-life, and show how it can be used to compare different NDR methods. Finally, we discuss limitations of our proposal and situations where further research is needed.

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"
    - "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 "genetic programming for evolving a front of interpretable models for data visualisation"):
  with members:
    - "evolving unsupervised deep neural networks for learning meaningful representations"
    - "can genetic programming do manifold learning too"

Connected through hyperedge HE-3 (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"
    - "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"
    - "a visual interaction framework for dimensionality reduction based data exploration"
    - "clustrophile a tool for visual clustering analysis"
    - "foresight recommending visual insights"

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)