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 "data dependent generalization bounds for multi class classification"):
  Abstract: In this paper, we study data-dependent generalization error bounds exhibiting a mild dependency on the number of classes, making them suitable for multi-class learning with a large number of label classes. The bounds generally hold for empirical multi-class risk minimization algorithms using an arbitrary norm as regularizer. Key to our analysis are new structural results for multi-class Gaussian complexities and empirical $\ell_\infty$-norm covering numbers, which exploit the Lipschitz continuity of the loss function with respect to the $\ell_2$- and $\ell_\infty$-norm, respectively. We establish data-dependent error bounds in terms of complexities of a linear function class defined on a finite set induced by training examples, for which we show tight lower and upper bounds. We apply the results to several prominent multi-class learning machines, exhibiting a tighter dependency on the number of classes than the state of the art. For instance, for the multi-class SVM by Crammer and Singer (2002), we obtain a data-dependent bound with a logarithmic dependency which significantly improves the previous square-root dependency. Experimental results are reported to verify the effectiveness of our theoretical findings.

Includes members:
  Member "generalization error bounds for learning to rank does the length of document lists matter":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an alternative cross entropy loss for learning to rank")
  Member "a vector contraction inequality for rademacher complexities":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning state abstractions for transfer in continuous control")
  Member "lshtc a benchmark for large scale text classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "integrating domain knowledge using hierarchies to improve deep classifiers")
  Member "multi class svms from tighter data dependent generalization bounds to novel algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning vector valued functions with local rademacher complexity")
  Member "efficient large scale video classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sketch2code generating a website from a paper mockup")
  Member "conditional probability tree estimation analysis and algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "efficient algorithms for set valued prediction in multi class classification")
  Member "maximum margin multiclass nearest neighbors":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "universal bayes consistency in metric spaces")
  Member "multiclass learnability and the erm principle":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "beyond ucb optimal and efficient contextual bandits with regression oracles")

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)