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: "humor in collective discourse unsupervised funniness detection in the new yorker cartoon caption contest"
  Abstract: The New Yorker publishes a weekly captionless cartoon. More than 5,000 readers submit captions for it. The editors select three of them and ask the readers to pick the funniest one. We describe an experiment that compares a dozen automatic methods for selecting the funniest caption. We show that negative sentiment, human-centeredness, and lexical centrality most strongly match the funniest captions, followed by positive sentiment. These results are useful for understanding humor and also in the design of more engaging conversational agents in text and multimodal (vision+text) systems. As part of this work, a large set of cartoons and captions is being made available to the community.

Connected through hyperedge HE-1 (induced by source paper "humor in word embeddings cockamamie gobbledegook for nincompoops"):
  with members:
    - "reverse engineering satire or paper on computational humor accepted despite making serious advances"
    - "the geometry of culture analyzing the meanings of class through word embeddings"
    - "word embeddings quantify 100 years of gender and ethnic stereotypes"
    - "advances in pre training distributed word representations"
    - "semantics derived automatically from language corpora contain human like biases"
    - "are word embedding based features useful for sarcasm detection"
    - "enriching word vectors with subword information"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"

Connected through hyperedge HE-2 (induced by source paper "punny captions witty wordplay in image descriptions"):
  with members:
    - "exploring the limits of language modeling"
    - "we are humor beings understanding and predicting visual humor"
    - "aligning books and movies towards story like visual explanations by watching movies and reading books"
    - "rethinking the inception architecture for computer vision"
    - "unifying visual semantic embeddings with multimodal neural language models"
    - "microsoft coco common objects in context"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "hashtagwars learning a sense of humor"):
  with members:
    - "reinforcement learning neural turing machines revised"
    - "adam a method for stochastic optimization"
    - "neural turing machines"
    - "generating sequences with recurrent neural networks"
    - "lexrank graph based lexical centrality as salience in text summarization"

Connected through hyperedge HE-4 (induced by source paper "we are humor beings understanding and predicting visual humor"):
  with members:
    - "understanding image virality"
    - "don t just listen use your imagination leveraging visual common sense for non visual tasks"
    - "vqa visual question answering"
    - "visual word2vec vis w2v learning visually grounded word embeddings using abstract scenes"
    - "imagenet large scale visual recognition challenge"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

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)