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 "adaptive metric dimensionality reduction"):
  Abstract: We study adaptive data-dependent dimensionality reduction in the context of supervised learning in general metric spaces. Our main statistical contribution is a generalization bound for Lipschitz functions in metric spaces that are doubling, or nearly doubling. On the algorithmic front, we describe an analogue of PCA for metric spaces: namely an efficient procedure that approximates the data's intrinsic dimension, which is often much lower than the ambient dimension. Our approach thus leverages the dual benefits of low dimensionality: (1) more efficient algorithms, e.g., for proximity search, and (2) more optimistic generalization bounds.

Includes members:
  Member "nearly optimal classification for semimetrics":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on statistical learning via the lens of compression")
  Member "near optimal sample compression for nearest neighbors":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the inverse voronoi problem in graphs")
  Member "a bayes consistent 1 nn classifier":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "robustness for non parametric classification a generic attack and defense")
  Member "efficient classification for metric data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "robustness for non parametric classification a generic attack and defense")
  Member "is margin preserved after random projection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the effectiveness of johnson lindenstrauss transform for high dimensional optimization with outliers")
  Member "tight sample complexity of large margin learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sharp analysis of low rank kernel matrix approximations")
  Member "planar earthmover is not in l_1":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "scalable nearest neighbor search for optimal transport")
  Member "sequential and parallel algorithms for mixed packing and covering":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "distributed assignment with limited communication for multi robot multi target tracking")

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.LG(Machine Learning)