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: "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.

Connected through hyperedge HE-1 (induced by source paper "towards automated infographic design deep learning based auto extraction of extensible timeline"):
  with members:
    - "synthetically trained icon proposals for parsing and summarizing infographics"
    - "vizml a machine learning approach to visualization recommendation"
    - "dvqa understanding data visualizations via question answering"
    - "focal loss for dense object detection"
    - "dynamic routing between capsules"
    - "learning visual importance for graphic designs and data visualizations"
    - "understanding infographics through textual and visual tag prediction"
    - "mask r cnn"

Connected through hyperedge HE-2 (induced by source paper "vismaker a question oriented visualization recommender system for data exploration"):
  with members:
    - "vizml a machine learning approach to visualization recommendation"

Connected through hyperedge HE-3 (induced by source paper "viznet towards a large scale visualization learning and benchmarking repository"):
  with members:
    - "idebench a benchmark for interactive data exploration"
    - "beyond heuristics learning visualization design"
    - "vizml a machine learning approach to visualization recommendation"
    - "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-4 (induced by source paper "task oriented optimal sequencing of visualization charts"):
  with members:
    - "vizml a machine learning approach to visualization recommendation"
    - "task based effectiveness of basic visualizations"

Connected through hyperedge HE-5 (induced by source paper "beyond heuristics learning visualization design"):
  with members:
    - "relational inductive biases deep learning and graph networks"
    - "task based effectiveness of basic visualizations"
    - "beagle automated extraction and interpretation of visualizations from the web"

Connected through hyperedge HE-6 (induced by source paper "vizml a machine learning approach to visualization recommendation"):
  with members:
    - "foresight rapid data exploration through guideposts"
    - "task based effectiveness of basic visualizations"
    - "beagle automated extraction and interpretation of visualizations from the web"

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)