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: "deepwriting making digital ink editable via deep generative modeling"
  Abstract: Digital ink promises to combine the flexibility and aesthetics of handwriting and the ability to process, search and edit digital text. Character recognition converts handwritten text into a digital representation, albeit at the cost of losing personalized appearance due to the technical difficulties of separating the interwoven components of content and style. In this paper, we propose a novel generative neural network architecture that is capable of disentangling style from content and thus making digital ink editable. Our model can synthesize arbitrary text, while giving users control over the visual appearance (style). For example, allowing for style transfer without changing the content, editing of digital ink at the word level and other application scenarios such as spell-checking and correction of handwritten text. We furthermore contribute a new dataset of handwritten text with fine-grained annotations at the character level and report results from an initial user evaluation.

Connected through hyperedge HE-1 (induced by source paper "the didi dataset digital ink diagram data"):
  with members:
    - "fast multi language lstm based online handwriting recognition"
    - "a neural representation of sketch drawings"

Connected through hyperedge HE-2 (induced by source paper "stcn stochastic temporal convolutional networks"):
  with members:
    - "the challenge of realistic music generation modelling raw audio at scale"
    - "stochastic wavenet a generative latent variable model for sequential data"
    - "fast decoding in sequence models using discrete latent variables"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "filtering variational objectives"
    - "learning hierarchical features from generative models"
    - "improved variational autoencoders for text modeling using dilated convolutions"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-3 (induced by source paper "generative adversarial network for handwritten text"):
  with members:
    - "adversarial generation of handwritten text images conditioned on sequences"
    - "generative adversarial networks an overview"
    - "improved techniques for training gans"
    - "a recurrent latent variable model for sequential data"
    - "generating sequences with recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "dynamic drawing guidance via electromagnetic haptic feedback"):
  with members:
    - "optimizing for aesthetically pleasing quadrotor camera motion"

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)