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: "taming near repeat calculation for crime analysis via cohesive subgraph computing"
  Abstract: Near repeat (NR) is a well known phenomenon in crime analysis assuming that crime events exhibit cor- relations within a given time and space frame. Traditional NR calculation generates 2 event pairs if 2 events happened within a given space and time limit. When the number of events is large, however, NR calculation is time consuming and how these pairs are organized are not yet explored. In this paper, we designed a new approach to calculate clusters of NR events efficiently. To begin with, R-tree is utilized to index crime events, a single event is represented by a vertex whereas edges are constructed by range querying the vertex in R-tree, and a graph is formed. Cohesive subgraph approaches are applied to identify the event chains. k-clique, k-truss, k- core plus DBSCAN algorithms are implemented in sequence with respect to their varied range of ability to find cohesive subgraphs. Real world crime data in Chicago, New York and Washington DC are utilized to conduct experiments. The experiment confirmed that near repeat is a solid effect in real big crime data by conducting Mapreduce empowered knox tests. The performance of 4 different algorithms are validated, while the quality of the algorithms are gauged by the distribution of number of cohesive subgraphs and their clustering coefficients. The proposed framework is the first to process the real crime data of million record scale, and is the first to detect NR events with size of more than 2.

Connected through hyperedge HE-1 (induced by source paper "crime in urban areas a data mining perspective"):
  with members:
    - "deep air learning interpolation prediction and feature analysis of fine grained air quality"
    - "deep spatio temporal residual networks for citywide crowd flows prediction"
    - "detecting criminal organizations in mobile phone networks"
    - "social network intelligence analysis to combat street gang violence"

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.DS(Data Structures and Algorithms)