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: "progressive data science potential and challenges"
  Abstract: Data science requires time-consuming iterative manual activities. In particular, activities such as data selection, preprocessing, transformation, and mining, highly depend on iterative trial-and-error processes that could be sped up significantly by providing quick feedback on the impact of changes. The idea of progressive data science is to compute the results of changes in a progressive manner, returning a first approximation of results quickly and allow iterative refinements until converging to a final result. Enabling the user to interact with the intermediate results allows an early detection of erroneous or suboptimal choices, the guided definition of modifications to the pipeline and their quick assessment. In this paper, we discuss the progressiveness challenges arising in different steps of the data science pipeline. We describe how changes in each step of the pipeline impact the subsequent steps and outline why progressive data science will help to make the process more effective. Computing progressive approximations of outcomes resulting from changes creates numerous research challenges, especially if the changes are made in the early steps of the pipeline. We discuss these challenges and outline first steps towards progressiveness, which, we argue, will ultimately help to significantly speed-up the overall data science process.

Connected through hyperedge HE-1 (induced by source paper "an incremental dimensionality reduction method for visualizing streaming multidimensional data"):
  with members:
    - "online visual analytics of text streams"
    - "an overview of gradient descent optimization algorithms"
    - "approximated and user steerable tsne for progressive visual analytics"
    - "adadelta an adaptive learning rate method"
    - "gmap drawing graphs as maps"

Connected through hyperedge HE-2 (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"
    - "approximated and user steerable tsne for progressive visual analytics"

Connected through hyperedge HE-3 (induced by source paper "dbpal weak supervision for learning a natural language interface to databases"):
  with members:
    - "sqlnet generating structured queries from natural language without reinforcement learning"
    - "neural ranking models with weak supervision"
    - "learning a neural semantic parser from user feedback"

Connected through hyperedge HE-4 (induced by source paper "gpgpu linear complexity t sne optimization"):
  with members:
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "t sne cuda gpu accelerated t sne and its applications to modern data"
    - "billion scale similarity search with gpus"
    - "pixelsne visualizing fast with just enough precision via pixel aligned stochastic neighbor embedding"
    - "progressive analytics a computation paradigm for exploratory data analysis"
    - "going deeper with convolutions"

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)