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 "similarity search combining query relaxation and diversification"):
  Abstract: In this paper, we combine query result diversity with query relaxation to balance the result number and result quality. Relaxation guarantees the number of the que-ry results, returning more relevance elements to the query if the results are too few, while the diversity tries to reduce the similarity among the returned results. By making a trade-off of similarity and diversity, we improve user experience. To achieve this goal, we design the goal function combining similarity and diversity. Aiming at this goal, we propose three algorithms. Among them, algorithms gen-Greedy and genCluster perform relaxation first and select part of the candidates to diversify. Pre-analysis of the dataset is made to choose a proper parameter of trade-off threshold. Moreover, we present algorithm CB2S that splits the datasets into smaller pieces using the cluster algorithm of k-means and process queries in several small sets to retrieve more diverse results. The balance of similarity and diversity is decided through setting a threshold, which has a default value and can be adjusted according to users' preference. The performance and efficiency of our system are demonstrated through extensive experiments based on various da-tasets.

Includes members:
  Member "disc diversity result diversification based on dissimilarity and coverage":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "exploration of interesting dense regions in spatial data")
  Member "providing diversity in k nearest neighbor query results":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "approximate k nearest neighbour based spatial clustering using k d tree")

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.DB(Databases)