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: "distributed clustering algorithm for spatial data mining"
  Abstract: Distributed data mining techniques and mainly distributed clustering are widely used in the last decade because they deal with very large and heterogeneous datasets which cannot be gathered centrally. Current distributed clustering approaches are normally generating global models by aggregating local results that are obtained on each site. While this approach mines the datasets on their locations the aggregation phase is complex, which may produce incorrect and ambiguous global clusters and therefore incorrect knowledge. In this paper we propose a new clustering approach for very large spatial datasets that are heterogeneous and distributed. The approach is based on K-means Algorithm but it generates the number of global clusters dynamically. Moreover, this approach uses an elaborated aggregation phase. The aggregation phase is designed in such a way that the overall process is efficient in time and memory allocation. Preliminary results show that the proposed approach produces high quality results and scales up well. We also compared it to two popular clustering algorithms and show that this approach is much more efficient.

Connected through hyperedge HE-1 (induced by source paper "distributed spatial data clustering as a new approach for big data analysis"):
  with members:
    - "hierarchical aggregation approach for distributed clustering of spatial datasets"
    - "efficient large scale clustering based on data partitioning"

Connected through hyperedge HE-2 (induced by source paper "efficient large scale clustering based on data partitioning"):
  with members:
    - "an efficient search tool for an anti money laundering application of an multi national bank s dataset"
    - "performance study of distributed apriori like frequent itemsets mining"
    - "knowledge map toward a new approach supporting the knowledge management in distributed data mining"
    - "treep a tree based p2p network architecture"
    - "dynamic task scheduling in computing cluster environments"

Connected through hyperedge HE-3 (induced by source paper "hierarchical aggregation approach for distributed clustering of spatial datasets"):
  with members:
    - "variance based clustering technique for distributed data mining applications"
    - "performance study of distributed apriori like frequent itemsets mining"

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.DB(Databases)