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: "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.

Connected through hyperedge HE-1 (induced by source paper "nearest neighbor sample compression efficiency consistency infinite dimensions"):
  with members:
    - "active nearest neighbor learning in metric spaces"
    - "maximum margin multiclass nearest neighbors"
    - "rates of convergence for nearest neighbor classification"
    - "a bayes consistent 1 nn classifier"
    - "on the geometry of similarity search dimensionality curse and concentration of measure"

Connected through hyperedge HE-2 (induced by source paper "active nearest neighbor learning in metric spaces"):
  with members:
    - "exact lower bounds for the agnostic probably approximately correct pac machine learning model"
    - "maximum margin multiclass nearest neighbors"
    - "minimax analysis of active learning"
    - "active learning on trees and graphs"
    - "efficient active learning of halfspaces an aggressive approach"

Connected through hyperedge HE-3 (induced by source paper "near optimal sample compression for nearest neighbors"):
  with members:
    - "nearest neighbor sample compression efficiency consistency infinite dimensions"
    - "active nearest neighbor learning in metric spaces"
    - "maximum margin multiclass nearest neighbors"
    - "rates of convergence for nearest neighbor classification"
    - "a bayes consistent 1 nn classifier"
    - "efficient classification for metric data"
    - "efficient regression in metric spaces via approximate lipschitz extension"
    - "fast construction of nets in low dimensional metrics and their applications"

Connected through hyperedge HE-4 (induced by source paper "maximum margin multiclass nearest neighbors"):
  with members:
    - "efficient classification for metric data"
    - "multiclass learnability and the erm principle"
    - "efficient regression in metric spaces via approximate lipschitz extension"
    - "fast construction of nets in low dimensional metrics and their applications"
    - "planar earthmover is not in l_1"

Connected through hyperedge HE-5 (induced by source paper "efficient regression in metric spaces via approximate lipschitz extension"):
  with members:
    - "algorithms for lipschitz learning on graphs"
    - "maximum margin multiclass nearest neighbors"
    - "a bayes consistent 1 nn classifier"
    - "efficient classification for metric data"
    - "from hierarchical partitions to hierarchical covers optimal fault tolerant spanners for doubling metrics"
    - "optimal euclidean spanners really short thin and lanky"
    - "fast construction of nets in low dimensional metrics and their applications"
    - "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)