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 "an embarrassingly simple baseline for extreme multi label prediction"):
  Abstract: The goal of eXtreme Multi-label Learning (XML) is to design and learn a model that can automatically annotate a given data point with the most relevant subset of labels from an extremely large label set. Recently, many techniques have been proposed for XML that achieve reasonable performance on benchmark datasets. Motivated by the complexities of these methods and their subsequent training requirements, in this paper we propose a simple baseline technique for this task. Precisely, we present a global feature embedding technique for XML that can easily scale to very large datasets containing millions of data points in very high-dimensional feature space, irrespective of number of samples and labels. Next we show how an ensemble of such global embeddings can be used to achieve further boost in prediction accuracies with only linear increase in training and prediction time. During testing, we assign the labels using a weighted k-nearest neighbour classifier in the embedding space. Experiments reveal that though conceptually simple, this technique achieves quite competitive results, and has training time of less than one minute using a single CPU core with 15.6 GB RAM even for large-scale datasets such as Amazon-3M.

Includes members:
  Member "single pass pca of large high dimensional data":
    also connected through other hyperedges:
      (none)
  Member "deep extreme multi label learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hyperbolic interaction model for hierarchical multi label classification")
  Member "experiments with random projection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "tensorized random projections")
  Member "large scale multi label learning with missing labels":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "pseudo labeling and negative feedback learning for large scale multi label domain classification")

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)