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: "exploring dimensionality reductions with forward and backward projections"
  Abstract: Dimensionality reduction is a common method for analyzing and visualizing high-dimensional data across domains. Dimensionality-reduction algorithms involve complex optimizations and the reduced dimensions computed by these algorithms generally lack clear relation to the initial data dimensions. Therefore, interpreting and reasoning about dimensionality reductions can be difficult. In this work, we introduce two interaction techniques, \textit{forward projection} and \textit{backward projection}, for reasoning dynamically about scatter plots of dimensionally reduced data. We also contribute two related visualization techniques, \textit{prolines} and \textit{feasibility map} to facilitate and enrich the effective use of the proposed interactions, which we integrate in a new tool called \textit{Praxis}. To evaluate our techniques, we first analyze their time and accuracy performance across varying sample and dimension sizes. We then conduct a user study in which twelve data scientists use \textit{Praxis} so as to assess the usefulness of the techniques in performing exploratory data analysis tasks. Results suggest that our visual interactions are intuitive and effective for exploring dimensionality reductions and generating hypotheses about the underlying data.

Connected through hyperedge HE-1 (induced by source paper "retainvis visual analytics with interpretable and interactive recurrent neural networks on electronic medical records"):
  with members:
    - "brain tumor segmentation with deep neural networks"
    - "understanding hidden memories of recurrent neural networks"
    - "direct manipulation visualization of deep networks"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "visualizing deep neural network decisions prediction difference analysis"
    - "dipole diagnosis prediction in healthcare via attention based bidirectional recurrent neural networks"
    - "towards better analysis of deep convolutional neural networks"
    - "the mythos of model interpretability"

Connected through hyperedge HE-2 (induced by source paper "a visual interaction framework for dimensionality reduction based data exploration"):
  with members:
    - "clustrophile a tool for visual clustering analysis"
    - "visualizing deep neural network decisions prediction difference analysis"
    - "fader networks manipulating images by sliding attributes"
    - "invertible conditional gans for image editing"
    - "multifaceted feature visualization uncovering the different types of features learned by each neuron in deep neural networks"
    - "not just a black box learning important features through propagating activation differences"
    - "rationalizing neural predictions"
    - "why should i trust you explaining the predictions of any classifier"

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)