human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "data2vis automatic generation of data visualizations using sequence to sequence recurrent neural networks"):
  Abstract: Rapidly creating effective visualizations using expressive grammars is challenging for users who have limited time and limited skills in statistics and data visualization. Even high-level, dedicated visualization tools often require users to manually select among data attributes, decide which transformations to apply, and specify mappings between visual encoding variables and raw or transformed attributes. #R##N#In this paper we introduce Data2Vis, a neural translation model for automatically generating visualizations from given datasets. We formulate visualization generation as a sequence to sequence translation problem where data specifications are mapped to visualization specifications in a declarative language (Vega-Lite). To this end, we train a multilayered attention-based recurrent neural network (RNN) with long short-term memory (LSTM) units on a corpus of visualization specifications. #R##N#Qualitative results show that our model learns the vocabulary and syntax for a valid visualization specification, appropriate transformations (count, bins, mean) and how to use common data selection patterns that occur within data visualizations. Data2Vis generates visualizations that are comparable to manually-created visualizations in a fraction of the time, with potential to learn more complex visualization strategies at scale.

Includes members:
  Member "beyond heuristics learning visualization design":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "viznet towards a large scale visualization learning and benchmarking repository")
  Member "vizml a machine learning approach to visualization recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "towards automated infographic design deep learning based auto extraction of extensible timeline")
  Member "tree to tree neural networks for program translation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "does syntax need to grow on trees sources of hierarchical inductive bias in sequence to sequence networks")
  Member "foresight recommending visual insights":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hillview a trillion cell spreadsheet for big data")
  Member "progressive growing of gans for improved quality stability and variation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "pix2code generating code from a graphical user interface screenshot":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "auto completion of user interface layout design using transformer based tree decoders")
  Member "massive exploration of neural machine translation architectures":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "meta embeddings based on self attention")
  Member "task based effectiveness of basic visualizations":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "viznet towards a large scale visualization learning and benchmarking repository")

Question: Please tell me which class the source paper 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)