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: "character sequence models for colorfulwords"
  Abstract: We present a neural network architecture to predict a point in color space from the sequence of characters in the color's name. Using large scale color--name pairs obtained from an online color design forum, we evaluate our model on a "color Turing test" and find that, given a name, the colors predicted by our model are preferred by annotators to color names created by humans. Our datasets and demo system are available online at colorlab.us.

Connected through hyperedge HE-1 (induced by source paper "learning of colors from color names distribution and point estimation"):
  with members:
    - "neural network with unbounded activation functions is universal approximator"
    - "colors in context a pragmatic neural model for grounded language understanding"
    - "learning to generalize to new compositions in image understanding"
    - "learning to generate compositional color descriptions"
    - "enriching word vectors with subword information"
    - "generative adversarial text to image synthesis"
    - "pixel recurrent neural networks"
    - "generating images from captions with attention"

Connected through hyperedge HE-2 (induced by source paper "trick or treat thematic reinforcement for artistic typography"):
  with members:
    - "contained neural style transfer for decorated logo generation"
    - "logo synthesis and manipulation with clustered generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "multi content gan for few shot font style transfer"
    - "a neural representation of sketch drawings"
    - "fine grained analysis of sentence embeddings using auxiliary prediction tasks"
    - "image to image translation with conditional adversarial networks"
    - "towards universal paraphrastic sentence embeddings"

Connected through hyperedge HE-3 (induced by source paper "lemotif abstract visual depictions of your emotional states in life"):
  with members:
    - "generative adversarial text to image synthesis"

Connected through hyperedge HE-4 (induced by source paper "coloring with words guiding image colorization through text based palette generation"):
  with members:
    - "interactive deep colorization with simultaneous global and local inputs"
    - "attention is all you need"
    - "text2action generative adversarial synthesis from language to action"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "pixcolor pixel recursive colorization"
    - "real time user guided image colorization with learned deep priors"
    - "stargan unified generative adversarial networks for multi domain image to image translation"
    - "colors in context a pragmatic neural model for grounded language understanding"

Connected through hyperedge HE-5 (induced by source paper "efficient human like semantic representations via the information bottleneck principle"):
  with members:
    - "opening the black box of deep neural networks via information"
    - "the symbol grounding problem"

Connected through hyperedge HE-6 (induced by source paper "colors in context a pragmatic neural model for grounded language understanding"):
  with members:
    - "learning to generate compositional color descriptions"
    - "reasoning about pragmatics with neural listeners and speakers"
    - "learning in the rational speech acts model"
    - "adam a method for stochastic optimization"
    - "show and tell a neural image caption generator"
    - "sequence to sequence learning with neural networks"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-7 (induced by source paper "learning to generate compositional color descriptions"):
  with members:
    - "theano a python framework for fast computation of mathematical expressions"
    - "learning in the rational speech acts model"
    - "generating sequences with recurrent neural networks"
    - "improving neural networks by preventing co adaptation of feature detectors"

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.CL(Computation and Language)