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: "learning to explain non standard english words and phrases"
  Abstract: We describe a data-driven approach for automatically explaining new, non-standard English expressions in a given sentence, building on a large dataset that includes 15 years of crowdsourced examples from this http URL. Unlike prior studies that focus on matching keywords from a slang dictionary, we investigate the possibility of learning a neural sequence-to-sequence model that generates explanations of unseen non-standard English expressions given context. We propose a dual encoder approach---a word-level encoder learns the representation of context, and a second character-level encoder to learn the hidden representation of the target non-standard expression. Our model can produce reasonable definitions of new non-standard English expressions given their context with certain confidence.

Connected through hyperedge HE-1 (induced by source paper "multi sense definition modeling using word sense decompositions"):
  with members:
    - "incorporating sememes into chinese definition modeling"
    - "conditional generators of words definitions"
    - "a mixture model for learning multi sense word embeddings"
    - "de conflated semantic representations"
    - "definition modeling learning to define word embeddings in natural language"
    - "linear algebraic structure of word senses with applications to polysemy"
    - "dynamic memory networks for visual and textual question answering"
    - "breaking sticks and ambiguities with adaptive skip gram"

Connected through hyperedge HE-2 (induced by source paper "learning to decipher hate symbols"):
  with members:
    - "generating sentences by editing prototypes"
    - "recognizing explicit and implicit hate speech using a weakly supervised two path bootstrapping approach"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "definition modeling learning to define word embeddings in natural language"
    - "ex machina personal attacks seen at scale"
    - "variational neural machine translation"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "learning to understand phrases by embedding the dictionary"

Connected through hyperedge HE-3 (induced by source paper "how to evaluate word representations of informal domain"):
  with members:
    - "adaptive region embedding for text classification"
    - "emo love and god making sense of urban dictionary a crowd sourced online dictionary"
    - "tweet2vec character based distributed representations for social media"
    - "tweet2vec learning tweet embeddings using character level cnn lstm encoder decoder"
    - "enriching word vectors with subword information"
    - "bag of tricks for efficient text classification"
    - "evaluating informal domain word representations with urbandictionary"
    - "bidirectional lstm crf models for sequence tagging"

Connected through hyperedge HE-4 (induced by source paper "learning to describe phrases with local and global contexts"):
  with members:
    - "conditional generators of words definitions"
    - "achieving open vocabulary neural machine translation with hybrid word character models"
    - "neural machine translation by jointly learning to align and translate"
    - "a survey of paraphrasing and textual entailment methods"

Connected through hyperedge HE-5 (induced by source paper "emo love and god making sense of urban dictionary a crowd sourced online dictionary"):
  with members:
    - "understanding abuse a typology of abusive language detection subtasks"
    - "diffusion of lexical change in social media"
    - "social networks that matter twitter under the microscope"
    - "novelty and collective attention"

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)