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: "tight sample complexity of large margin learning"
  Abstract: We obtain a tight distribution-specific characterization of the sample complexity of large-margin classification with L_2 regularization: We introduce the \gamma-adapted-dimension, which is a simple function of the spectrum of a distribution's covariance matrix, and show distribution-specific upper and lower bounds on the sample complexity, both governed by the \gamma-adapted-dimension of the source distribution. We conclude that this new quantity tightly characterizes the true sample complexity of large-margin classification. The bounds hold for a rich family of sub-Gaussian distributions.

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

Connected through hyperedge HE-2 (induced by source paper "sharp analysis of low rank kernel matrix approximations"):
  with members:
    - "matrix coherence and the nystrom method"
    - "improved bounds for the nystrom method with application to kernel classification"
    - "fast approximation of matrix coherence and statistical leverage"
    - "randomized algorithms for matrices and data"
    - "self concordant analysis for logistic regression"
    - "an improved approximation algorithm for the column subset selection problem"

Connected through hyperedge HE-3 (induced by source paper "efficient active learning of halfspaces an aggressive approach"):
  with members:
    - "importance weighted active learning"

Connected through hyperedge HE-4 (induced by source paper "active learning of halfspaces under a margin assumption"):
  with members:
    - "importance weighted active learning"

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)