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 "nearest neighbor sample compression efficiency consistency infinite dimensions"):
  Abstract: We examine the Bayes-consistency of a recently proposed 1-nearest-neighbor-based multiclass learning algorithm. This algorithm is derived from sample compression bounds and enjoys the statistical advantages of tight, fully empirical generalization bounds, as well as the algorithmic advantages of a faster runtime and memory savings. We prove that this algorithm is strongly Bayes-consistent in metric spaces with finite doubling dimension --- the first consistency result for an efficient nearest-neighbor sample compression scheme. Rather surprisingly, we discover that this algorithm continues to be Bayes-consistent even in a certain infinite-dimensional setting, in which the basic measure-theoretic conditions on which classic consistency proofs hinge are violated. This is all the more surprising, since it is known that $k$-NN is not Bayes-consistent in this setting. We pose several challenging open problems for future research.

Includes members:
  Member "active nearest neighbor learning in metric spaces":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an adjusted nearest neighbor algorithm maximizing the f measure from imbalanced data")
  Member "maximum margin multiclass nearest neighbors":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "universal bayes consistency in metric spaces")
  Member "rates of convergence for nearest neighbor classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "robustness for non parametric classification a generic attack and defense")
  Member "a bayes consistent 1 nn classifier":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "robustness for non parametric classification a generic attack and defense")
  Member "adaptive metric dimensionality reduction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "active nearest neighbor learning in metric spaces")
  Member "on the geometry of similarity search dimensionality curse and concentration of measure":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "universal bayes consistency in metric spaces")

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)