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: "efficient regression in metric spaces via approximate lipschitz extension"
  Abstract: We present a framework for performing efficient regression in general metric spaces. Roughly speaking, our regressor predicts the value at a new point by computing a Lipschitz extension --- the smoothest function consistent with the observed data --- after performing structural risk minimization to avoid overfitting. We obtain finite-sample risk bounds with minimal structural and noise assumptions, and a natural speed-precision tradeoff. The offline (learning) and online (prediction) stages can be solved by convex programming, but this naive approach has runtime complexity $O(n^3)$, which is prohibitive for large datasets. We design instead a regression algorithm whose speed and generalization performance depend on the intrinsic dimension of the data, to which the algorithm adapts. While our main innovation is algorithmic, the statistical results may also be of independent interest.

Connected through hyperedge HE-1 (induced by source paper "truly optimal euclidean spanners"):
  with members:
    - "on the spanning and routing ratio of theta four"
    - "odd yao yao graphs are not spanners"
    - "greedy spanners are optimal in doubling metrics"
    - "minor free graphs have light spanners"
    - "the greedy spanner is existentially optimal"
    - "almost all even yao yao graphs are spanners"
    - "a light metric spanner"
    - "upper bounds on the spanning ratio of constrained theta graphs"

Connected through hyperedge HE-2 (induced by source paper "regression via kirszbraun extension with applications to imitation learning"):
  with members:
    - "an algorithmic perspective on imitation learning"
    - "nonlinear dimension reduction via outer bi lipschitz extensions"
    - "learning monocular reactive uav control in cluttered natural environments"
    - "a reduction of imitation learning and structured prediction to no regret online learning"
    - "electrical flows laplacian systems and faster approximation of maximum flow in undirected graphs"

Connected through hyperedge HE-3 (induced by source paper "regression via kirszbraun extension"):
  with members:
    - "nonlinear dimension reduction via outer bi lipschitz extensions"
    - "electrical flows laplacian systems and faster approximation of maximum flow in undirected graphs"
    - "fast construction of nets in low dimensional metrics and their applications"
    - "nearly linear time algorithms for graph partitioning graph sparsification and solving linear systems"

Connected through hyperedge HE-4 (induced by source paper "exploration of a graph based density sensitive metric"):
  with members:
    - "strong coresets for k median and subspace approximation goodbye dimension"
    - "adaptive metrics for adaptive samples"
    - "massively parallel algorithms and hardness for single linkage clustering under ell_p distances"
    - "polynomial representations of threshold functions and algorithmic applications"
    - "approximating nearest neighbor distances"
    - "optimal euclidean spanners really short thin and lanky"
    - "semi supervised learning with density based distances"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-5 (induced by source paper "a light metric spanner"):
  with members:
    - "the traveling salesman problem low dimensionality implies a polynomial time approximation scheme"
    - "light spanners for snowflake metrics"
    - "from hierarchical partitions to hierarchical covers optimal fault tolerant spanners for doubling metrics"
    - "optimal euclidean spanners really short thin and lanky"
    - "light spanners in bounded pathwidth graphs"
    - "shallow low and light trees and tight lower bounds for euclidean spanners"
    - "fast construction of nets in low dimensional metrics and their applications"

Connected through hyperedge HE-6 (induced by source paper "algorithms for lipschitz learning on graphs"):
  with members:
    - "efficient classification for metric data"
    - "faster approximate lossy generalized flow via interior point algorithms"
    - "nearly linear time algorithms for graph partitioning graph sparsification and solving linear systems"

Connected through hyperedge HE-7 (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"
    - "adaptive metric dimensionality reduction"
    - "fast construction of nets in low dimensional metrics and their applications"

Connected through hyperedge HE-8 (induced by source paper "maximum margin multiclass nearest neighbors"):
  with members:
    - "efficient classification for metric data"
    - "multiclass learnability and the erm principle"
    - "adaptive metric dimensionality reduction"
    - "fast construction of nets in low dimensional metrics and their applications"
    - "planar earthmover is not in l_1"

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)