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 "less but better generalization enhancement of ordinal embedding via distributional margin"):
  Abstract: In the absence of prior knowledge, ordinal embedding methods obtain new representation for items in a low-dimensional Euclidean space via a set of quadruple-wise comparisons. These ordinal comparisons often come from human annotators, and sufficient comparisons induce the success of classical approaches. However, collecting a large number of labeled data is known as a hard task, and most of the existing work pay little attention to the generalization ability with insufficient samples. Meanwhile, recent progress in large margin theory discloses that rather than just maximizing the minimum margin, both the margin mean and variance, which characterize the margin distribution, are more crucial to the overall generalization performance. To address the issue of insufficient training samples, we propose a margin distribution learning paradigm for ordinal embedding, entitled Distributional Margin based Ordinal Embedding (\textit{DMOE}). Precisely, we first define the margin for ordinal embedding problem. Secondly, we formulate a concise objective function which avoids maximizing margin mean and minimizing margin variance directly but exhibits the similar effect. Moreover, an Augmented Lagrange Multiplier based algorithm is customized to seek the optimal solution of \textit{DMOE} effectively. Experimental studies on both simulated and real-world datasets are provided to show the effectiveness of the proposed algorithm.

Includes members:
  Member "optimal margin distribution machine":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "effects of the optimisation of the margin distribution on generalisation in deep architectures")
  Member "learning concept embeddings with combined human machine expertise":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "probabilistic end to end noise correction for learning with noisy labels")
  Member "efficient online relative comparison kernel learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient data analytics on augmented similarity triplets")
  Member "cost effective hits for relative similarity comparisons":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficient data analytics on augmented similarity triplets")
  Member "large margin distribution machine":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "maximal margin distribution support vector regression with coupled constraints based convex optimization")
  Member "adaptively learning the crowd kernel":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "interpretable off policy evaluation in reinforcement learning by highlighting influential transitions")
  Member "on the doubt about margin explanation of boosting":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "margin maximization as lossless maximal compression")

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)