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: "visual affect around the world a large scale multilingual visual sentiment ontology"
  Abstract: Every culture and language is unique. Our work expressly focuses on the uniqueness of culture and language in relation to human affect, specifically sentiment and emotion semantics, and how they manifest in social multimedia. We develop sets of sentiment- and emotion-polarized visual concepts by adapting semantic structures called adjective-noun pairs, originally introduced by Borth et al. (2013), but in a multilingual context. We propose a new language-dependent method for automatic discovery of these adjective-noun constructs. We show how this pipeline can be applied on a social multimedia platform for the creation of a large-scale multilingual visual sentiment concept ontology (MVSO). Unlike the flat structure in Borth et al. (2013), our unified ontology is organized hierarchically by multilingual clusters of visually detectable nouns and subclusters of emotionally biased versions of these nouns. In addition, we present an image-based prediction task to show how generalizable language-specific models are in a multilingual context. A new, publicly available dataset of >15.6K sentiment-biased visual concepts across 12 languages with language-specific detector banks, >7.36M images and their metadata is also released.

Connected through hyperedge HE-1 (induced by source paper "affective computing for large scale heterogeneous multimedia data a survey"):
  with members:
    - "pdanet polarity consistent deep attention network for fine grained visual emotion regression"
    - "polysemous visual semantic embedding for cross modal retrieval"
    - "meld a multimodal multi party dataset for emotion recognition in conversations"
    - "multi attention recurrent network for human communication comprehension"
    - "a data driven approach to mid level perceptual musical feature modeling"
    - "depechemood a bilingual emotion lexicon built through simple yet powerful techniques"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "looking beyond a clever narrative visual context and attention are primary drivers of affect in video advertisements"

Connected through hyperedge HE-2 (induced by source paper "the focus aspect polarity model for predicting subjective noun attributes in images"):
  with members:
    - "deep cross residual learning for multitask visual recognition"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "from visual attributes to adjectives through decompositional distributional semantics"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-3 (induced by source paper "bam the behance artistic media dataset for recognition beyond photography"):
  with members:
    - "domain adaptation for visual applications a comprehensive survey"
    - "sketch based manga retrieval using manga109 dataset"
    - "synthesizing the preferred inputs for neurons in neural networks via deep generator networks"
    - "ssd single shot multibox detector"
    - "building a large scale dataset for image emotion recognition the fine print and the benchmark"
    - "the amazing mysteries of the gutter drawing inferences between panels in comic book narratives"
    - "fine grained categorization and dataset bootstrapping using deep metric learning with humans in the loop"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-4 (induced by source paper "more cat than cute interpretable prediction of adjective noun pairs"):
  with members:
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "going deeper for multilingual visual sentiment detection"
    - "deep cross residual learning for multitask visual recognition"
    - "densely connected convolutional networks"
    - "from pixels to sentiment fine tuning cnns for visual sentiment prediction"
    - "mapping images to sentiment adjective noun pairs with factorized neural nets"
    - "robust image sentiment analysis using progressively trained and domain transferred deep networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

Connected through hyperedge HE-5 (induced by source paper "visual and textual sentiment analysis using deep fusion convolutional neural networks"):
  with members:
    - "robust image sentiment analysis using progressively trained and domain transferred deep networks"
    - "distributed representations of sentences and documents"
    - "effective use of word order for text categorization with convolutional neural networks"
    - "convolutional neural networks for sentence classification"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "from pixels to sentiment fine tuning cnns for visual sentiment prediction"):
  with members:
    - "adaptive computation time for recurrent neural networks"
    - "cultural event recognition with visual convnets and temporal models"
    - "robust image sentiment analysis using progressively trained and domain transferred deep networks"
    - "diving deep into sentiment understanding fine tuned cnns for visual sentiment prediction"
    - "deep residual learning for image recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "analyzing the performance of multilayer neural networks for object recognition"
    - "how transferable are features in deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "automatic attribute discovery with neural activations"):
  with members:
    - "a deep learning based fast image saliency detection algorithm"
    - "understanding neural networks through deep visualization"
    - "robust image sentiment analysis using progressively trained and domain transferred deep networks"
    - "deep carving discovering visual attributes by carving deep neural nets"
    - "automatic concept discovery from parallel text and visual corpora"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "deep residual learning for image recognition"
    - "deep learning for object saliency detection and image segmentation"

Connected through hyperedge HE-8 (induced by source paper "multilingual visual sentiment concept matching"):
  with members:
    - "a fast and accurate unconstrained face detector"
    - "visual word2vec vis w2v learning visually grounded word embeddings using abstract scenes"
    - "combining language and vision with a multimodal skip gram model"
    - "the beauty of capturing faces rating the quality of digital portraits"
    - "unifying visual semantic embeddings with multimodal neural language models"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "multilingual models for compositional distributed semantics"
    - "explain images with multimodal recurrent neural networks"

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.MM(Multimedia)