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 "the effectiveness of johnson lindenstrauss transform for high dimensional optimization with outliers"):
  Abstract: {\em Johnson-Lindenstrauss (JL) Transform} is one of the most popular methods for dimension reduction. In this paper, we study the effectiveness of JL transform for solving the high dimensional optimization problems with outliers. We focus on two fundamental optimization problems: {\em $k$-center clustering with outliers} and {\em SVM with outliers}. In general, the time complexity for dealing with outliers in high dimensional space could be very large. Based on some novel insights in geometry, we prove that the complexities of these two problems can be significantly reduced through JL transform. In the experiments, we compare JL transform with several other well known dimension reduction methods, and study their performances on synthetic and real datasets.

Includes members:
  Member "greedy strategy works for k center clustering with outliers and coreset construction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on metric dbscan with low doubling dimension")
  Member "wild patterns ten years after the rise of adversarial machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "vulnerabilities of connectionist ai applications evaluation and defence")
  Member "manipulating machine learning poisoning attacks and countermeasures for regression learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "the non uniform k center problem":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on metric dbscan with low doubling dimension")
  Member "adversarial machine learning at scale":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adversarial ranking attack and defense")
  Member "approximation and streaming algorithms for projective clustering via random projections":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "coresets for gaussian mixture models of any shape")
  Member "recovering the optimal solution by dual random projection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "improved subsampled randomized hadamard transform for linear svm")
  Member "random projections for linear support vector machines":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "stable sparse subspace embedding for dimensionality reduction")

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.CG(Computational Geometry)