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: "smelly maps the digital life of urban smellscapes"
  Abstract: Smell has a huge influence over how we perceive places. Despite its importance, smell has been crucially overlooked by urban planners and scientists alike, not least because it is difficult to record and analyze at scale. One of the authors of this paper has ventured out in the urban world and conducted smellwalks in a variety of cities: participants were exposed to a range of different smellscapes and asked to record their experiences. As a result, smell-related words have been collected and classified, creating the first dictionary for urban smell. Here we explore the possibility of using social media data to reliably map the smells of entire cities. To this end, for both Barcelona and London, we collect geo-referenced picture tags from Flickr and Instagram, and geo-referenced tweets from Twitter. We match those tags and tweets with the words in the smell dictionary. We find that smell-related words are best classified in ten categories. We also find that specific categories (e.g., industry, transport, cleaning) correlate with governmental air quality indicators, adding validity to our study.

Connected through hyperedge HE-1 (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"
    - "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"
    - "the shortest path to happiness recommending beautiful quiet and happy routes in the city"

Connected through hyperedge HE-2 (induced by source paper "smell pittsburgh engaging community citizen science for air quality"):
  with members:
    - "when human computer interaction meets community citizen science"
    - "community empowered air quality monitoring system"
    - "industrial smoke detection and visualization"
    - "why should i trust you explaining the predictions of any classifier"
    - "the emotional and chromatic layers of urban smells"

Connected through hyperedge HE-3 (induced by source paper "smell pittsburgh community empowered mobile smell reporting system"):
  with members:
    - "community empowered air quality monitoring system"
    - "industrial smoke detection and visualization"
    - "why should i trust you explaining the predictions of any classifier"
    - "the emotional and chromatic layers of urban smells"

Connected through hyperedge HE-4 (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"
    - "the shortest path to happiness recommending beautiful quiet and happy routes in the city"

Connected through hyperedge HE-5 (induced by source paper "quantifying location sociality"):
  with members:
    - "measuring urban deprivation from user generated content"
    - "dawn of the selfie era the whos wheres and hows of selfies on instagram"
    - "topological properties and temporal dynamics of place networks in urban environments"
    - "the digital life of walkable streets"
    - "foodporn obesity patterns in culinary interactions"
    - "the shortest path to happiness recommending beautiful quiet and happy routes in the city"
    - "where businesses thrive predicting the impact of the olympic games on local retailers through location based services data"
    - "location prediction communities speak louder than friends"

Connected through hyperedge HE-6 (induced by source paper "chatty maps constructing sound maps of urban areas from social media data"):
  with members:
    - "the digital life of walkable streets"
    - "the shortest path to happiness recommending beautiful quiet and happy routes in the city"
    - "crowdsourcing a word emotion association lexicon"

Connected through hyperedge HE-7 (induced by source paper "the emotional and chromatic layers of urban smells"):
  with members:
    - "the shortest path to happiness recommending beautiful quiet and happy routes in the city"
    - "crowdsourcing a word emotion association lexicon"

Connected through hyperedge HE-8 (induced by source paper "location prediction communities speak louder than friends"):
  with members:
    - "group colocation behavior in technological social networks"
    - "community detection in networks with node attributes"
    - "discovering social circles in ego 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.SI(Social and Information Networks)