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 analytics a computation paradigm for exploratory data analysis"
  Abstract: Exploring data requires a fast feedback loop from the analyst to the system, with a latency below about 10 seconds because of human cognitive limitations. When data becomes large or analysis becomes complex, sequential computations can no longer be completed in a few seconds and data exploration is severely hampered. This article describes a novel computation paradigm called Progressive Computation for Data Analysis or more concisely Progressive Analytics, that brings at the programming language level a low-latency guarantee by performing computations in a progressive fashion. Moving this progressive computation at the language level relieves the programmer of exploratory data analysis systems from implementing the whole analytics pipeline in a progressive way from scratch, streamlining the implementation of scalable exploratory data analysis systems. This article describes the new paradigm through a prototype implementation called ProgressiVis, and explains the requirements it implies through examples.

Connected through hyperedge HE-1 (induced by source paper "hillview a trillion cell spreadsheet for big data"):
  with members:
    - "big data visualization tools"
    - "foresight recommending visual insights"
    - "visualization aware sampling for very large databases"
    - "a hierarchical aggregation framework for efficient multilevel visual exploration and analysis"
    - "rapid sampling for visualizations with ordering guarantees"
    - "a tutorial on principal component analysis"
    - "bottom k and priority sampling set similarity and subset sums with minimal independence"
    - "blinkdb queries with bounded errors and bounded response times on very large data"

Connected through hyperedge HE-2 (induced by source paper "rsatree distribution aware data representation of large scale tabular datasets for flexible visual query"):
  with members:
    - "the case for learned index structures"
    - "visualization aware sampling for very large databases"
    - "rapid sampling for visualizations with ordering guarantees"
    - "blinkdb queries with bounded errors and bounded response times on very large data"

Connected through hyperedge HE-3 (induced by source paper "visualizing and analyzing entity activity on the bitcoin network"):
  with members:
    - "the unreasonable effectiveness of address clustering"
    - "bitcoin transaction graph analysis"

Connected through hyperedge HE-4 (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"
    - "revisiting reuse in main memory database systems"
    - "an interactive machine learning framework"

Connected through hyperedge HE-5 (induced by source paper "gpgpu linear complexity t sne optimization"):
  with members:
    - "progressive data science potential and challenges"
    - "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"
    - "going deeper with convolutions"

Connected through hyperedge HE-6 (induced by source paper "infiniviz interactive visual exploration using progressive bin refinement"):
  with members:
    - "effortless data exploration with zenvisage an expressive and interactive visual analytics system"
    - "visualization aware sampling for very large databases"
    - "a sampling algebra for aggregate estimation"

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)