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 "a practical framework for solving center based clustering with outliers"):
  Abstract: Clustering has many important applications in computer science, but real-world datasets often contain outliers. Moreover, the existence of outliers can make the clustering problems to be much more challenging. In this paper, we propose a practical framework for solving the problems of $k$-center/median/means clustering with outliers. The framework actually is very simple, where we just need to take a small sample from input and run existing approximation algorithm on the sample. However, our analysis is fundamentally different from the previous sampling based ideas. In particular, the size of the sample is independent of the input data size and dimensionality. To explain the effectiveness of random sampling in theory, we introduce a "significance" criterion and prove that the performance of our framework depends on the significance degree of the given instance. Actually, our result can be viewed as a new step along the direction of beyond worst-case analysis in terms of clustering with outliers. The experiments suggest that our framework can achieve comparable clustering result with existing methods, but greatly reduce the running time.

Includes members:
  Member "beyond worst case analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "escaping cannibalization correlation robust pricing for a unit demand buyer")
  Member "solving k center clustering with outliers in mapreduce and streaming almost as accurately as sequentially":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "coreset based strategies for robust center type problems")
  Member "varepsilon coresets for clustering with outliers in doubling metrics":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "layered sampling for robust optimization problems")
  Member "distributed partial clustering":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fast noise removal for k means clustering")
  Member "approximation schemes for clustering with outliers":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "layered sampling for robust optimization problems")
  Member "constant approximation for k median and k means with outliers via iterative rounding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "layered sampling for robust optimization problems")
  Member "the non uniform k center problem":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the effectiveness of johnson lindenstrauss transform for high dimensional optimization with outliers")
  Member "approximating k median via pseudo approximation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "fair correlation clustering")

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.DS(Data Structures and Algorithms)