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: "we are humor beings understanding and predicting visual humor"
  Abstract: Humor is an integral part of human lives. Despite being tremendously impactful, it is perhaps surprising that we do not have a detailed understanding of humor yet. As interactions between humans and AI systems increase, it is imperative that these systems are taught to understand subtleties of human expressions such as humor. In this work, we are interested in the question - what content in a scene causes it to be funny? As a first step towards understanding visual humor, we analyze the humor manifested in abstract scenes and design computational models for them. We collect two datasets of abstract scenes that facilitate the study of humor at both the scene-level and the object-level. We analyze the funny scenes and explore the different types of humor depicted in them via human studies. We model two tasks that we believe demonstrate an understanding of some aspects of visual humor. The tasks involve predicting the funniness of a scene and altering the funniness of a scene. We show that our models perform well quantitatively, and qualitatively through human studies. Our datasets are publicly available.

Connected through hyperedge HE-1 (induced by source paper "why does a visual question have different answers"):
  with members:
    - "vizwiz grand challenge answering visual questions from blind people"
    - "crowdsourcing ground truth for medical relation extraction"
    - "an analysis of visual question answering algorithms"
    - "learning to disambiguate by asking discriminative questions"
    - "the promise of premise harnessing question premises in visual question answering"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "crowdsourcing question answer meaning representations"
    - "bottom up and top down attention for image captioning and visual question answering"

Connected through hyperedge HE-2 (induced by source paper "xslue a benchmark and analysis platform for cross style language understanding and evaluation"):
  with members:
    - "deep contextualized word representations for detecting sarcasm and irony"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer"
    - "detecting and explaining causes from text for a time series event"
    - "automated hate speech detection and the problem of offensive language"
    - "sarcasm sign interpreting sarcasm with sentiment based monolingual machine translation"
    - "controlling linguistic style aspects in neural language generation"

Connected through hyperedge HE-3 (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-4 (induced by source paper "totally looks like how humans compare compared to machines"):
  with members:
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "modeling human categorization of natural images using deep feature representations"
    - "recent advance in content based image retrieval a literature survey"
    - "comparing deep neural networks against humans object recognition when the signal gets weaker"
    - "adapting deep network features to capture psychological representations"
    - "understanding and mapping natural beauty"
    - "densely connected convolutional networks"
    - "understanding image virality"

Connected through hyperedge HE-5 (induced by source paper "it takes two to tango towards theory of ai s mind"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "hadamard product for low rank bilinear pooling"
    - "attentive explanations justifying decisions and pointing to the evidence"
    - "who is mistaken"
    - "towards transparent ai systems interpreting visual question answering models"
    - "learning language games through interaction"
    - "analyzing the behavior of visual question answering models"
    - "inception v4 inception resnet and the impact of residual connections on learning"

Connected through hyperedge HE-6 (induced by source paper "automatic understanding of image and video advertisements"):
  with members:
    - "end to end saliency mapping via probability distribution prediction"
    - "deep reconstruction classification networks for unsupervised domain adaptation"
    - "walk and learn facial attribute representation learning from egocentric video and contextual data"
    - "what happens if learning to predict the effect of forces in images"
    - "learning aligned cross modal representations from weakly aligned data"
    - "domain separation networks"
    - "unsupervised domain adaptation with residual transfer networks"
    - "whittlesearch interactive image search with relative attribute feedback"

Connected through hyperedge HE-7 (induced by source paper "punny captions witty wordplay in image descriptions"):
  with members:
    - "exploring the limits of language modeling"
    - "humor in collective discourse unsupervised funniness detection in the new yorker cartoon caption contest"
    - "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-8 (induced by source paper "understanding and mapping natural beauty"):
  with members:
    - "a unified model for near and remote sensing"
    - "planet photo geolocation with convolutional neural networks"
    - "horizon lines in the wild"
    - "predicting ground level scene layout from aerial imagery"
    - "understanding image virality"
    - "wide area image geolocalization with aerial reference imagery"
    - "the digital life of walkable streets"
    - "the shortest path to happiness recommending beautiful quiet and happy routes in the city"

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.CV(Computer Vision and Pattern Recognition)