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: "choosing the right home location definition method for the given dataset"
  Abstract: Ever since first mobile phones equipped with GPS came to the market, knowing the exact user location has become a holy grail of almost every service that lives in the digital world. Starting with the idea of location based services, nowadays it is not only important to know where users are in real time, but also to be able predict where they will be in future. Moreover, it is not enough to know user location in form of latitude longitude coordinates provided by GPS devices, but also to give a place its meaning (i.e., semantically label it), in particular detecting the most probable home location for the given user. The aim of this paper is to provide novel insights on differences among the ways how different types of human digital trails represent the actual mobility patterns and therefore the differences between the approaches interpreting those trails for inferring said patterns. Namely, with the emergence of different digital sources that provide information about user mobility, it is of vital importance to fully understand that not all of them capture exactly the same picture. With that being said, in this paper we start from an example showing how human mobility patterns described by means of radius of gyration are different for Flickr social network and dataset of bank card transactions. Rather than capturing human movements closer to their homes, Flickr more often reveals people travel mode. Consequently, home location inferring methods used in both cases cannot be the same. We consider several methods for home location definition known from the literature and demonstrate that although for bank card transactions they provide highly consistent results, home location definition detection methods applied to Flickr dataset happen to be way more sensitive to the method selected, stressing the paramount importance of adjusting the method to the specific dataset being used.

Connected through hyperedge HE-1 (induced by source paper "assessing the quality of home detection from mobile phone data for official statistics"):
  with members:
    - "a comparison of spatial based targeted disease containment strategies using mobile phone data"
    - "an analytical framework to nowcast well being using mobile phone data"
    - "d4d senegal the second mobile phone data for development challenge"
    - "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-2 (induced by source paper "profiling presence patterns and segmenting user locations from cell phone data"):
  with members:
    - "analysis of tourism dynamics and special events through mobile phone metadata"
    - "exploring universal patterns in human home work commuting from mobile phone data"

Connected through hyperedge HE-3 (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"
    - "predicting regional economic indices using big data of individual bank card transactions"
    - "urban magnetism through the lens of geo tagged photography"
    - "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-4 (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"
    - "predicting regional economic indices using big data of individual bank card transactions"
    - "urban magnetism through the lens of geo tagged photography"
    - "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-5 (induced by source paper "sublinear scaling of country attractiveness observed from flickr dataset"):
  with members:
    - "yfcc100m the new data in multimedia research"
    - "predicting regional economic indices using big data of individual bank card transactions"
    - "urban magnetism through the lens of geo tagged photography"
    - "scaling of city attractiveness for foreign visitors through big data of human economical and social media activity"

Connected through hyperedge HE-6 (induced by source paper "predicting regional economic indices using big data of individual bank card transactions"):
  with members:
    - "urban magnetism through the lens of geo tagged photography"
    - "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"

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)