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: "beyond heuristics learning visualization design"
  Abstract: In this paper, we describe a research agenda for deriving design principles directly from data. We argue that it is time to go beyond manually curated and applied visualization design guidelines. We propose learning models of visualization design from data collected using graphical perception studies and build tools powered by the learned models. To achieve this vision, we need to 1) develop scalable methods for collecting training data, 2) collect different forms of training data, 3) advance interpretability of machine learning models, and 4) develop adaptive models that evolve as more data becomes available.

Connected through hyperedge HE-1 (induced by source paper "viznet towards a large scale visualization learning and benchmarking repository"):
  with members:
    - "idebench a benchmark for interactive data exploration"
    - "vizml a machine learning approach to visualization recommendation"
    - "data2vis automatic generation of data visualizations using sequence to sequence recurrent neural networks"
    - "task based effectiveness of basic visualizations"
    - "beagle automated extraction and interpretation of visualizations from the web"
    - "squad 100 000 questions for machine comprehension of text"
    - "boomerang rebounding the consequences of reputation feedback on crowdsourcing platforms"
    - "viziometrics analyzing visual information in the scientific literature"

Connected through hyperedge HE-2 (induced by source paper "data2vis automatic generation of data visualizations using sequence to sequence recurrent neural networks"):
  with members:
    - "vizml a machine learning approach to visualization recommendation"
    - "tree to tree neural networks for program translation"
    - "foresight recommending visual insights"
    - "progressive growing of gans for improved quality stability and variation"
    - "pix2code generating code from a graphical user interface screenshot"
    - "massive exploration of neural machine translation architectures"
    - "task based effectiveness of basic visualizations"
    - "a syntactic neural model for general purpose code generation"

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)