human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "mining for spatially near communities in geo located social networks"):
  Abstract: Current approaches to community detection in social networks often ignore the spatial location of the nodes. In this paper, we look to extract spatially-near communities in a social network. We introduce a new metric to measure the quality of a community partition in a geolocated social networks called "spatially-near modularity" a value that increases based on aspects of the network structure but decreases based on the distance between nodes in the communities. We then look to find an optimal partition with respect to this measure - which should be an "ideal" community with respect to both social ties and geographic location. Though an NP-hard problem, we introduce two heuristic algorithms that attempt to maximize this measure and outperform non-geographic community finding by an order of magnitude. Applications to counter-terrorism are also discussed.

Includes members:
  Member "a latent parameter node centric model for spatial networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mining for geographically disperse communities in social networks by leveraging distance modularity")
  Member "mining for geographically disperse communities in social networks by leveraging distance modularity":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey of community search over big graphs")
  Member "social network intelligence analysis to combat street gang violence":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "crime in urban areas a data mining perspective")
  Member "extending modularity by incorporating distance functions in the null model":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mining for geographically disperse communities in social networks by leveraging distance modularity")
  Member "demon a local first discovery method for overlapping communities":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "self falsifiable hierarchical detection of overlapping communities on social networks")

Question: Please tell me which class the source paper 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)