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 "self paced multi label learning with diversity"):
  Abstract: The major challenge of learning from multi-label data has arisen from the overwhelming size of label space which makes this problem NP-hard. This problem can be alleviated by gradually involving easy to hard tags into the learning process. Besides, the utilization of a diversity maintenance approach avoids overfitting on a subset of easy labels. In this paper, we propose a self-paced multi-label learning with diversity (SPMLD) which aims to cover diverse labels with respect to its learning pace. In addition, the proposed framework is applied to an efficient correlation-based multi-label method. The non-convex objective function is optimized by an extension of the block coordinate descent algorithm. Empirical evaluations on real-world datasets with different dimensions of features and labels imply the effectiveness of the proposed predictive model.

Includes members:
  Member "self paced deep learning for weakly supervised object detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "low budget unsupervised label query through domain alignment enforcement")
  Member "active self paced learning for cost effective and progressive face identification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "low budget unsupervised label query through domain alignment enforcement")
  Member "multi label learning with global and local label correlation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "joint ranking svm and binary relevance with robust low rank learning for multi label classification")
  Member "self paced multi task learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adaptive activation network and functional regularization for efficient and flexible deep multi task learning")
  Member "a unified view of multi label performance measures":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "joint ranking svm and binary relevance with robust low rank learning for multi label classification")
  Member "manopt a matlab toolbox for optimization on manifolds":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "joint constellation design for the two user non coherent multiple access channel")
  Member "large scale multi label learning with missing labels":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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)