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: "geo located twitter as proxy for global mobility patterns"
  Abstract: Pervasive presence of location-sharing services made it possible for researchers to gain an unprecedented access to the direct records of human activity in space and time. This article analyses geo-located Twitter messages in order to uncover global patterns of human mobility. Based on a dataset of almost a billion tweets recorded in 2012, we estimate the volume of international travelers by country of residence. Mobility profiles of different nations were examined based on such characteristics as mobility rate, radius of gyration, diversity of destinations, and inflow–outflow balance. Temporal patterns disclose the universally valid seasons of increased international mobility and the particular character of international travels of different nations. Our analysis of the community structure of the Twitter mobility network reveals spatially cohesive regions that follow the regional division of the world. We validate our result using global tourism statistics and mobility models provided by other authors and argue that Twitter is exceptionally useful for understanding and quantifying global mobility patterns.

Connected through hyperedge HE-1 (induced by source paper "transfer learning from an auxiliary discriminative task for unsupervised anomaly detection"):
  with members:
    - "efficient gan based anomaly detection"
    - "high order graph convolutional recurrent neural network a deep learning framework for network scale traffic learning and forecasting"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "cnn features are also great at unsupervised classification"
    - "learning representations for automatic colorization"
    - "global multi layer network of human mobility"
    - "deep spatio temporal residual networks for citywide crowd flows prediction"

Connected through hyperedge HE-2 (induced by source paper "a large scale empirical study of geotagging behavior on twitter"):
  with members:
    - "parameterized convolutional neural networks for aspect level sentiment classification"
    - "location order recovery in trails with low temporal resolution"
    - "aspect level sentiment classification with attention over attention neural networks"
    - "on predicting geolocation of tweets using convolutional neural networks"
    - "a probabilistic framework for location inference from social media"
    - "twitter user geolocation using a unified text and network prediction model"
    - "where in the world are you geolocation and language identification in twitter"

Connected through hyperedge HE-3 (induced by source paper "evaluation of biases in self reported demographic and psychometric information traditional versus facebook based surveys"):
  with members:
    - "predicting demographics moral foundations and human values from digital behaviours"
    - "studying migrant assimilation through facebook interests"
    - "using facebook ads audiences for global lifestyle disease surveillance promises and limitations"
    - "processing social media messages in mass emergency a survey"
    - "big questions for social media big data representativeness validity and other methodological pitfalls"

Connected through hyperedge HE-4 (induced by source paper "a survey of location prediction on twitter"):
  with members:
    - "continuous representation of location for geolocation and lexical dialectology using mixture density networks"
    - "a neural model for user geolocation and lexical dialectology"
    - "density estimation for geolocation via convolutional mixture density network"
    - "a survey of point of interest recommendation in location based social networks"
    - "exploiting text and network context for geolocation of social media users"
    - "analysis of named entity recognition and linking for tweets"
    - "twitter user geolocation using a unified text and network prediction model"
    - "a survey of location inference techniques on twitter"

Connected through hyperedge HE-5 (induced by source paper "exposure to urban parks improves affect and reduces negativity on twitter"):
  with members:
    - "the emotional arcs of stories are dominated by six basic shapes"
    - "measuring the happiness of large scale written expression songs blogs and presidents"

Connected through hyperedge HE-6 (induced by source paper "sensing the chinese diaspora how mobile apps can provide insights into global migration flows"):
  with members:
    - "from migration corridors to clusters the value of google data for migration studies"

Connected through hyperedge HE-7 (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-8 (induced by source paper "scaling laws in geo located twitter data"):
  with members:
    - "social sensing of floods in the uk"
    - "a survey of location inference techniques on twitter"
    - "confounds and consequences in geotagged twitter data"
    - "scaling of city attractiveness for foreign visitors through big data of human economical and social media activity"
    - "performance of social network sensors during hurricane sandy"
    - "big questions for social media big data representativeness validity and other methodological pitfalls"
    - "geotagging one hundred million twitter accounts with total variation minimization"
    - "is the sample good enough comparing data from twitter s streaming api with twitter s firehose"

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)