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: "urban magnetism through the lens of geo tagged photography"
  Abstract: There is an increasing trend of people leaving digital traces through social media. This reality opens new horizons for urban studies. With this kind of data, researchers and urban planners can detect many aspects of how people live in cities and can also suggest how to transform cities into more efficient and smarter places to live in. In particular, their digital trails can be used to investigate tastes of individuals, and what attracts them to live in a particular city or to spend their vacation there. In this paper we propose an unconventional way to study how people experience the city, using information from geotagged photographs that people take at different locations. We compare the spatial behavior of residents and tourists in 10 most photographed cities all around the world. The study was conducted on both a global and local level. On the global scale we analyze the 10 most photographed cities and measure how attractive each city is for people visiting it from other cities within the same country or from abroad. For the purpose of our analysis we construct the users’ mobility network and measure the strength of the links between each pair of cities as a level of attraction of people living in one city (i.e., origin) to the other city (i.e., destination). On the local level we study the spatial distribution of user activity and identify the photographed hotspots inside each city. The proposed methodology and the results of our study are a low cost mean to characterize touristic activity within a certain location and can help cities strengthening their touristic potential.

Connected through hyperedge HE-1 (induced by source paper "fine grained land use classification at the city scale using ground level images"):
  with members:
    - "building instance classification using street view images"
    - "large scale mapping of human activity using geo tagged videos"
    - "hidden two stream convolutional networks for action recognition"
    - "quantitative comparison of open source data for fine grain mapping of land use"
    - "webvision challenge visual learning and understanding with web data"
    - "eurosat a novel dataset and deep learning benchmark for land use and land cover classification"
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "adaptive deep pyramid matching for remote sensing scene classification"

Connected through hyperedge HE-2 (induced by source paper "large scale mapping of human activity using geo tagged videos"):
  with members:
    - "exploiting feature and class relationships in video categorization with regularized deep neural networks"
    - "deep local video feature for action recognition"
    - "hidden two stream convolutional networks for action recognition"
    - "densenet for dense flow"
    - "guided optical flow learning"
    - "spatio temporal sentiment hotspot detection using geotagged photos"
    - "depth2action exploring embedded depth for large scale action recognition"
    - "temporal segment networks towards good practices for deep action recognition"

Connected through hyperedge HE-3 (induced by source paper "impact of urban technology deployments on local commercial activity"):
  with members:
    - "predicting regional economic indices using big data of individual bank card transactions"
    - "scaling of city attractiveness for foreign visitors through big data of human economical and social media activity"
    - "geo located twitter as proxy for global mobility patterns"
    - "exploring universal patterns in human home work commuting from mobile phone data"
    - "delineating geographical regions with networks of human interactions in an extensive set of countries"

Connected through hyperedge HE-4 (induced by source paper "the network structure of visited locations according to geotagged social media photos"):
  with members:
    - "learning points and routes to recommend trajectories"
    - "yfcc100m the new data in multimedia research"
    - "geo located twitter as proxy for global mobility patterns"
    - "uncovering patterns of inter urban trip and spatial interaction from social media check in data"

Connected through hyperedge HE-5 (induced by source paper "twitter activity timeline as a signature of urban neighborhood"):
  with members:
    - "scaling of city attractiveness for foreign visitors through big data of human economical and social media activity"
    - "delineating geographical regions with networks of human interactions in an extensive set of countries"

Connected through hyperedge HE-6 (induced by source paper "global multi layer network of human mobility"):
  with members:
    - "scaling of foreign attractiveness for countries and states"
    - "yfcc100m the new data in multimedia research"
    - "choosing the right home location definition method for the given dataset"
    - "predicting regional economic indices using big data of individual bank card transactions"
    - "scaling of city attractiveness for foreign visitors through big data of human economical and social media activity"
    - "geo located twitter as proxy for global mobility patterns"
    - "general optimization technique for high quality community detection in complex networks"
    - "exploring universal patterns in human home work commuting from mobile phone data"

Connected through hyperedge HE-7 (induced by source paper "scaling of foreign attractiveness for countries and states"):
  with members:
    - "global multi layer network of human mobility"
    - "yfcc100m the new data in multimedia research"
    - "sublinear scaling of country attractiveness observed from flickr dataset"
    - "choosing the right home location definition method for the given dataset"
    - "predicting regional economic indices using big data of individual bank card transactions"
    - "scaling of city attractiveness for foreign visitors through big data of human economical and social media activity"
    - "geo located twitter as proxy for global mobility patterns"
    - "exploring universal patterns in human home work commuting from mobile phone data"

Connected through hyperedge HE-8 (induced by source paper "sublinear scaling of country attractiveness observed from flickr dataset"):
  with members:
    - "yfcc100m the new data in multimedia research"
    - "choosing the right home location definition method for the given dataset"
    - "predicting regional economic indices using big data of individual bank card transactions"
    - "scaling of city attractiveness for foreign visitors through big data of human economical and social media activity"

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)