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 "near optimal sample compression for nearest neighbors"):
  Abstract: We present the first sample compression algorithm for nearest neighbors with non-trivial performance guarantees. We complement these guarantees by demonstrating almost matching hardness lower bounds, which show that our bound is nearly optimal. Our result yields new insight into margin-based nearest neighbor classification in metric spaces and allows us to significantly sharpen and simplify existing bounds. Some encouraging empirical results are also presented.

Includes members:
  Member "nearest neighbor sample compression efficiency consistency infinite dimensions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "robustness for non parametric classification a generic attack and defense")
  Member "active nearest neighbor learning in metric spaces":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (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-4 (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-5 (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-6 (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-7 (induced by source paper "nearest neighbor sample compression efficiency consistency infinite dimensions")
  Member "efficient regression in metric spaces via approximate lipschitz extension":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "truly optimal euclidean spanners")

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)