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: "twitter as a source of global mobility patterns for social good"
  Abstract: Data on human spatial distribution and movement is essential for understanding and analyzing social systems. However existing sources for this data are lacking in various ways; difficult to access, biased, have poor geographical or temporal resolution, or are significantly delayed. In this paper, we describe how geolocation data from Twitter can be used to estimate global mobility patterns and address these shortcomings. These findings will inform how this novel data source can be harnessed to address humanitarian and development efforts.

Connected through hyperedge HE-1 (induced by source paper "to stay or to leave churn prediction for urban migrants in the initial period"):
  with members:
    - "urban dreams of migrants a case study of migrant integration in shanghai"
    - "motifs in temporal networks"
    - "the death and life of great italian cities a mobile phone data perspective"
    - "assembling thefacebook using heterogeneity to understand online social network assembly"
    - "all who wander on the prevalence and characteristics of multi community engagement"
    - "the digital life of walkable streets"
    - "from amateurs to connoisseurs modeling the evolution of user expertise through online reviews"

Connected through hyperedge HE-2 (induced by source paper "continuous representation of location for geolocation and lexical dialectology using mixture density networks"):
  with members:
    - "density estimation for geolocation via convolutional mixture density network"
    - "estimating local commuting patterns from geolocated twitter data"
    - "exploiting text and network context for geolocation of social media users"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "urban dreams of migrants a case study of migrant integration in shanghai"):
  with members:
    - "the digital life of walkable streets"

Connected through hyperedge HE-4 (induced by source paper "predicting human mobility through the assimilation of social media traces into mobility models"):
  with members:
    - "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"

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)