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: "delineating geographical regions with networks of human interactions in an extensive set of countries"
  Abstract: Large-scale networks of human interaction, in particular country-wide telephone call networks, can be used to redraw geographical maps by applying algorithms of topological community detection. The geographic projections of the emerging areas in a few recent studies on single regions have been suggested to share two distinct properties: first, they are cohesive, and second, they tend to closely follow socio-economic boundaries and are similar to existing political regions in size and number. Here we use an extended set of countries and clustering indices to quantify overlaps, providing ample additional evidence for these observations using phone data from countries of various scales across Europe, Asia, and Africa: France, the UK, Italy, Belgium, Portugal, Saudi Arabia, and Ivory Coast. In our analysis we use the known approach of partitioning country-wide networks, and an additional iterative partitioning of each of the first level communities into sub-communities, revealing that cohesiveness and matching of official regions can also be observed on a second level if spatial resolution of the data is high enough. The method has possible policy implications on the definition of the borderlines and sizes of administrative regions.

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"
    - "choosing the right home location definition method for the given dataset"
    - "d4d senegal the second mobile phone data for development challenge"
    - "exploring universal patterns in human home work commuting from mobile phone data"

Connected through hyperedge HE-2 (induced by source paper "the human geography of twitter"):
  with members:
    - "depicting urban boundaries from a mobility network of spatial interactions a case study of great britain with geo located twitter data"
    - "dialectometric analysis of language variation in twitter"
    - "scaling laws in geo located twitter data"
    - "demographic dialectal variation in social media a case study of african american english"

Connected through hyperedge HE-3 (induced by source paper "depicting urban boundaries from a mobility network of spatial interactions a case study of great britain with geo located twitter data"):
  with members:
    - "explore spatiotemporal and demographic characteristics of human mobility via twitter a case study of chicago"
    - "geo located twitter as proxy for global mobility patterns"
    - "uncovering patterns of inter urban trip and spatial interaction from social media check in data"
    - "a scalable framework for spatiotemporal analysis of location based social media data"
    - "exploring universal patterns in human home work commuting from mobile phone data"
    - "a classification for community discovery methods in complex networks"

Connected through hyperedge HE-4 (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"
    - "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 "twitter activity timeline as a signature of urban neighborhood"):
  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"

Connected through hyperedge HE-6 (induced by source paper "deriving human activity from geo located data by ontological and statistical reasoning"):
  with members:
    - "impact of the spatial context on human communication activity"
    - "characterization of behavioral patterns exploiting description of geographical areas"

Connected through hyperedge HE-7 (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"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "choosing the right home location definition method for the given 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"
    - "geo located twitter as proxy for global mobility patterns"
    - "where in the world are you geolocation and language identification in twitter"
    - "exploring universal patterns in human home work commuting from mobile phone data"
    - "multiple location profiling for users and relationships from social network and content"

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)