human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "the inverse voronoi problem in graphs"):
  with members:
    - "on the minimum consistent subset problem"
    - "voronoi diagrams on planar graphs and computing the diameter in deterministic tilde o n 5 3 time"
    - "better tradeoffs for exact distance oracles in planar graphs"
    - "subquadratic algorithms for the diameter and the sum of pairwise distances in planar graphs"
    - "optimal parameterized algorithms for planar facility location problems using voronoi diagrams"
    - "voronoi game on graphs"
    - "graph induced complex on point data"
    - "minimum weight triangulation is np hard"

Connected through hyperedge HE-2 (induced by source paper "on the minimum consistent subset problem"):
  with members:
    - "optimal parameterized algorithms for planar facility location problems using voronoi diagrams"

Connected through hyperedge HE-3 (induced by source paper "nearly optimal classification for semimetrics"):
  with members:
    - "maximum margin multiclass nearest neighbors"
    - "fast construction of nets in low dimensional metrics and their applications"
    - "planar earthmover is not in l_1"

Connected through hyperedge HE-4 (induced by source paper "a bayes consistent 1 nn classifier"):
  with members:
    - "maximum margin multiclass nearest neighbors"
    - "rates of convergence for nearest neighbor classification"
    - "efficient classification for metric data"

Connected through hyperedge HE-5 (induced by source paper "adaptive metric dimensionality reduction"):
  with members:
    - "nearly optimal classification for semimetrics"
    - "a bayes consistent 1 nn classifier"
    - "efficient classification for metric data"
    - "is margin preserved after random projection"
    - "tight sample complexity of large margin learning"
    - "planar earthmover is not in l_1"
    - "sequential and parallel algorithms for mixed packing and covering"

Question: Please tell me which class the center node 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)