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: "text segmentation with character level text embeddings"
  Abstract: Learning word representations has recently seen much success in computational linguistics. However, assuming sequences of word tokens as input to linguistic analysis is often unjustified. For many languages word segmentation is a non-trivial task and naturally occurring text is sometimes a mixture of natural language strings and other character data. We propose to learn text representations directly from raw character sequences by training a Simple recurrent Network to predict the next character in text. The network uses its hidden layer to evolve abstract representations of the character sequences it sees. To demonstrate the usefulness of the learned text embeddings, we use them as features in a supervised character level text segmentation and labeling task: recognizing spans of text containing programming language code. By using the embeddings as features we are able to substantially improve over a baseline which uses only surface character n-grams.

Connected through hyperedge HE-1 (induced by source paper "nonsymbolic text representation"):
  with members:
    - "character based neural machine translation"
    - "online segment to segment neural transduction"
    - "a joint model for word embedding and word morphology"
    - "single model encoder decoder with explicit morphological representation for reinflection"
    - "gated word character recurrent language model"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "enriching word vectors with subword information"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"

Connected through hyperedge HE-2 (induced by source paper "charagram embedding words and sentences via character n grams"):
  with members:
    - "character based neural machine translation"
    - "counter fitting word vectors to linguistic constraints"
    - "achieving open vocabulary neural machine translation with hybrid word character models"
    - "learning distributed representations of sentences from unlabelled data"
    - "exploring the limits of language modeling"
    - "theano a python framework for fast computation of mathematical expressions"
    - "a character level decoder without explicit segmentation for neural machine translation"
    - "character based neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "from phonemes to images levels of representation in a recurrent neural model of visually grounded language learning"):
  with members:
    - "neural language correction with character based attention"
    - "representation of linguistic form and function in recurrent neural networks"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "automatic description generation from images a survey of models datasets and evaluation measures"
    - "pixel recurrent neural networks"
    - "a character level decoder without explicit segmentation for neural machine translation"
    - "finding function in form compositional character models for open vocabulary word representation"
    - "skip thought vectors"

Connected through hyperedge HE-4 (induced by source paper "semantic tagging with deep residual networks"):
  with members:
    - "byte based language identification with deep convolutional networks"
    - "identity mappings in deep residual networks"
    - "very deep convolutional networks for text classification"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "multi task cross lingual sequence tagging from scratch"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "a character level decoder without explicit segmentation for neural machine translation"
    - "multilingual language processing from bytes"

Connected through hyperedge HE-5 (induced by source paper "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"):
  with members:
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "character level convolutional networks for text classification"
    - "multilingual language processing from bytes"
    - "finding function in form compositional character models for open vocabulary word representation"
    - "improved transition based parsing by modeling characters instead of words with lstms"
    - "bidirectional lstm crf models for sequence tagging"
    - "part of speech tagging with bidirectional long short term memory recurrent neural network"
    - "character aware neural language models"

Connected through hyperedge HE-6 (induced by source paper "robsut wrod reocginiton via semi character recurrent neural network"):
  with members:
    - "sentence level grammatical error identification as sequence to sequence correction"
    - "character based neural machine translation"
    - "improved transition based parsing by modeling characters instead of words with lstms"
    - "character aware neural language models"

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)