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 "ldsm logarithm depth streaming multi label decision trees"):
  Abstract: We consider multi-label classification where the goal is to annotate each data point with the most relevant $\textit{subset}$ of labels from an extremely large label set. Efficient annotation can be achieved with balanced tree predictors, i.e. trees with logarithmic-depth in the label complexity, whose leaves correspond to labels. Designing prediction mechanism with such trees for real data applications is non-trivial as it needs to accommodate sending examples to multiple leaves while at the same time sustain high prediction accuracy. In this paper we develop the LdSM algorithm for the construction and training of multi-label decision trees, where in every node of the tree we optimize a novel objective function that favors balanced splits, maintains high class purity of children nodes, and allows sending examples to multiple directions but with a penalty that prevents tree over-growth. Each node of the tree is trained once the previous one is completed leading to a streaming approach for training. We analyze the proposed method theoretically and show that minimizing the objective leads to pure and balanced data splits. Furthermore, we prove that optimizing it results in the monotonic decrease of the error with every split. Experimental results on benchmark data sets demonstrate that our approach achieves high prediction accuracy with logarithmic-depth trees and position LdSM as a competitive tool among existing state-of-the-art tree-based approaches in terms of the statistical performance and prediction time.

Includes members:
  Member "bonsai diverse and shallow trees for extreme multi label classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "x bert extreme multi label text classification with using bidirectional encoder representations from transformers")
  Member "a no regret generalization of hierarchical softmax to extreme multi label classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "large scale multi label text classification on eu legislation")
  Member "deep extreme multi label learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hyperbolic interaction model for hierarchical multi label classification")
  Member "logarithmic time online multiclass prediction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "joint optimization of tree based index and deep model for recommender systems")
  Member "normalized online learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on feature normalization and data augmentation")
  Member "large scale multi label learning with missing labels":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "pseudo labeling and negative feedback learning for large scale multi label domain classification")
  Member "the landmark selection method for multiple output prediction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "towards scalable and reliable capsule networks for challenging nlp applications")
  Member "multi label prediction via compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "crowd scene analysis by output encoding")

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)