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: "multi label learning with global and local label correlation"
  Abstract: It is well-known that exploiting label correlations is important to multi-label learning. Existing approaches either assume that the label correlations are global and shared by all instances; or that the label correlations are local and shared only by a data subset. In fact, in the real-world applications, both cases may occur that some label correlations are globally applicable and some are shared only in a local group of instances. Moreover, it is also a usual case that only partial labels are observed, which makes the exploitation of the label correlations much more difficult. That is, it is hard to estimate the label correlations when many labels are absent. In this paper, we propose a new multi-label approach GLOCAL dealing with both the full-label and the missing-label cases, exploiting global and local label correlations simultaneously, through learning a latent label representation and optimizing label manifolds. The extensive experimental studies validate the effectiveness of our approach on both full-label and missing-label data.

Connected through hyperedge HE-1 (induced by source paper "joint ranking svm and binary relevance with robust low rank learning for multi label classification"):
  with members:
    - "a unified view of multi label performance measures"
    - "cnn rnn a unified framework for multi label image classification"
    - "large scale multi label learning with missing labels"

Connected through hyperedge HE-2 (induced by source paper "partial multi label learning with label and feature collaboration"):
  with members:
    - "multi label learning with missing labels using mixed dependency graphs"

Connected through hyperedge HE-3 (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"
    - "self paced multi task learning"
    - "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-4 (induced by source paper "tag recommendation by word level tag sequence modeling"):
  with members:
    - "attention is all you need"
    - "cnn rnn a unified framework for multi label image classification"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "deep convolutional ranking for multilabel image annotation"

Connected through hyperedge HE-5 (induced by source paper "vicsom visual clues from social media for psychological assessment"):
  with members:
    - "instagram photos reveal predictive markers of depression"
    - "efficient estimation of word representations in vector space"
    - "multi instance multi label learning"

Connected through hyperedge HE-6 (induced by source paper "a multi task gradient descent method for multi label learning"):
  with members:
    - "collaboration based multi label learning"
    - "a survey on multi task learning"
    - "diffusion adaptation over networks"

Connected through hyperedge HE-7 (induced by source paper "collaboration based multi label learning"):
  with members:
    - "exploit bounding box annotations for multi label object recognition"

Connected through hyperedge HE-8 (induced by source paper "a baseline for multi label image classification using an ensemble of deep convolutional neural networks"):
  with members:
    - "data augmentation by pairing samples for images classification"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "snapshot ensembles train 1 get m for free"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "kill two birds with one stone weakly supervised neural network for image annotation and tag refinement"
    - "mixup beyond empirical risk minimization"
    - "multi label zero shot human action recognition via joint latent embedding"
    - "learning spatial regularization with image level supervisions for multi label image classification"

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)