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: "the shortest path to happiness recommending beautiful quiet and happy routes in the city"
  Abstract: When providing directions to a place, web and mobile mapping services are all able to suggest the shortest route. The goal of this work is to automatically suggest routes that are not only short but also emotionally pleasant. To quantify the extent to which urban locations are pleasant, we use data from a crowd-sourcing platform that shows two street scenes in London (out of hundreds), and a user votes on which one looks more beautiful, quiet, and happy. We consider votes from more than 3.3K individuals and translate them into quantitative measures of location perceptions. We arrange those locations into a graph upon which we learn pleasant routes. Based on a quantitative validation, we find that, compared to the shortest routes, the recommended ones add just a few extra walking minutes and are indeed perceived to be more beautiful, quiet, and happy. To test the generality of our approach, we consider Flickr metadata of more than 3.7M pictures in London and 1.3M in Boston, compute proxies for the crowdsourced beauty dimension (the one for which we have collected the most votes), and evaluate those proxies with 30 participants in London and 54 in Boston. These participants have not only rated our recommendations but have also carefully motivated their choices, providing insights for future work.

Connected through hyperedge HE-1 (induced by source paper "facelift a transparent deep learning framework to beautify urban scenes"):
  with members:
    - "augmented reality meets computer vision efficient data generation for urban driving scenes"
    - "disentangled person image generation"
    - "unsupervised cross domain image generation"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "synthesizing the preferred inputs for neurons in neural networks via deep generator networks"
    - "generating images with perceptual similarity metrics based on deep networks"
    - "are safer looking neighborhoods more lively a multimodal investigation into urban life"
    - "deep learning the city quantifying urban perception at a global scale"

Connected through hyperedge HE-2 (induced by source paper "language in our time an empirical analysis of hashtags"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "tagvisor a privacy advisor for sharing hashtags"
    - "hierarchical graph representation learning with differentiable pooling"
    - "you are your metadata identification and obfuscation of social media users using metadata information"
    - "analyzing the digital traces of political manipulation the 2016 russian interference twitter campaign"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "twitter for sparking a movement reddit for sharing the moment metoo through the lens of social media"
    - "is saki delicious the food perception gap on instagram and its relation to health"

Connected through hyperedge HE-3 (induced by source paper "outdoorsent sentiment analysis of urban outdoor images by using semantic and deep features"):
  with members:
    - "gender matters analyzing global cultural gender preferences for venues using social sensing"
    - "yolo9000 better faster stronger"
    - "from pixels to sentiment fine tuning cnns for visual sentiment prediction"
    - "yfcc100m the new data in multimedia research"
    - "smelly maps the digital life of urban smellscapes"
    - "robust image sentiment analysis using progressively trained and domain transferred deep networks"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-4 (induced by source paper "farsa fully automated roadway safety assessment"):
  with members:
    - "a unified model for near and remote sensing"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "understanding and mapping natural beauty"
    - "deep learning the city quantifying urban perception at a global scale"
    - "torontocity seeing the world with a million eyes"
    - "detecting vanishing points using global image context in a non manhattan world"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "graph based recommendations from data representation to feature extraction and application"):
  with members:
    - "graph based collaborative ranking"

Connected through hyperedge HE-6 (induced by source paper "embedding geographic locations for modelling the natural environment using flickr tags and structured data"):
  with members:
    - "poincar e embeddings for learning hierarchical representations"
    - "conceptnet 5 5 an open multilingual graph of general knowledge"
    - "part of speech relevance weights for learning word embeddings"
    - "node2vec scalable feature learning for networks"
    - "bag of tricks for efficient text classification"
    - "complex embeddings for simple link prediction"
    - "order embeddings of images and language"
    - "embedding entities and relations for learning and inference in knowledge bases"

Connected through hyperedge HE-7 (induced by source paper "saferoute learning to navigate streets safely in an urban environment"):
  with members:
    - "semi parametric topological memory for navigation"
    - "look before you leap bridging model free and model based reinforcement learning for planned ahead vision and language navigation"
    - "learning to navigate in cities without a map"
    - "teaching a machine to read maps with deep reinforcement learning"
    - "node2vec scalable feature learning for networks"
    - "reinforcement learning neural turing machines revised"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "the new urban success how culture pays"):
  with members:
    - "chatty maps constructing sound maps of urban areas from social media data"
    - "smelly maps the digital life of urban smellscapes"

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.SI(Social and Information Networks)