human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "self paced multi task learning"
  Abstract: In this paper, we propose a novel multi-task learning (MTL) framework, called Self-Paced Multi-Task Learning (SPMTL). Different from previous works treating all tasks and instances equally when training, SPMTL attempts to jointly learn the tasks by taking into consideration the complexities of both tasks and instances. This is inspired by the cognitive process of human brain that often learns from the easy to the hard. We construct a compact SPMTL formulation by proposing a new task-oriented regularizer that can jointly prioritize the tasks and the instances. Thus it can be interpreted as a self-paced learner for MTL. A simple yet effective algorithm is designed for optimizing the proposed objective function. An error bound for a simplified formulation is also analyzed theoretically. Experimental results on toy and real-world datasets demonstrate the effectiveness of the proposed approach, compared to the state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "adaptive activation network and functional regularization for efficient and flexible deep multi task learning"):
  with members:
    - "a sufficient condition for convergences of adam and rmsprop"
    - "taskonomy disentangling task transfer learning"
    - "beyond shared hierarchies deep multitask learning through soft layer ordering"
    - "deep multi task representation learning a tensor factorisation approach"
    - "cross stitch networks for multi task learning"
    - "youtube 8m a large scale video classification benchmark"
    - "deep residual learning for image recognition"
    - "learning multiple tasks with multilinear relationship networks"

Connected through hyperedge HE-2 (induced by source paper "self paced multi label learning with diversity"):
  with members:
    - "self paced deep learning for weakly supervised object detection"
    - "active self paced learning for cost effective and progressive face identification"
    - "multi label learning with global and local label correlation"
    - "a unified view of multi label performance measures"
    - "manopt a matlab toolbox for optimization on manifolds"
    - "large scale multi label learning with missing labels"

Connected through hyperedge HE-3 (induced by source paper "pyramidal person re identification via multi loss dynamic training"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "towards good practices on building effective cnn baseline model for person re identification"
    - "multi level factorisation net for person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "harmonious attention network for person re identification"
    - "dual attention matching network for context aware feature sequence based person re identification"
    - "deep group shuffling random walk for person re identification"
    - "in defense of the triplet loss for person re identification"

Connected through hyperedge HE-4 (induced by source paper "self paced multi task clustering"):
  with members:
    - "deep density based image clustering"
    - "unsupervised deep embedding for clustering analysis"
    - "heterogeneous multi task learning for human pose estimation with deep convolutional neural network"
    - "convex discriminative multitask clustering"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-5 (induced by source paper "understanding self paced learning under concave conjugacy theory"):
  with members:
    - "self paced learning an implicit regularization perspective"
    - "are all training examples equally valuable"

Connected through hyperedge HE-6 (induced by source paper "target curricula via selection of minimum feature sets a case study in boolean networks"):
  with members:
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "knowledge matters importance of prior information for optimization"
    - "learning generalization and functional entropy in random automata networks"

Question: Please tell me which class the center node 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)