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: "maximum margin output coding"
  Abstract: In this paper we study output coding for multi-label prediction. For a multi-label output coding to be discriminative, it is important that codewords for different label vectors are significantly different from each other. In the meantime, unlike in traditional coding theory, codewords in output coding are to be predicted from the input, so it is also critical to have a predictable label encoding. #R##N#To find output codes that are both discriminative and predictable, we first propose a max-margin formulation that naturally captures these two properties. We then convert it to a metric learning formulation, but with an exponentially large number of constraints as commonly encountered in structured prediction problems. Without a label structure for tractable inference, we use overgenerating (i.e., relaxation) techniques combined with the cutting plane method for optimization. #R##N#In our empirical study, the proposed output coding scheme outperforms a variety of existing multi-label prediction methods for image, text and music classification.

Connected through hyperedge HE-1 (induced by source paper "prototypical networks for multi label learning"):
  with members:
    - "multi label image recognition with graph convolutional networks"
    - "collaboration based multi label learning"
    - "prototypical networks for few shot learning"
    - "exploit bounding box annotations for multi label object recognition"
    - "adam a method for stochastic optimization"
    - "large scale multi label learning with missing labels"

Connected through hyperedge HE-2 (induced by source paper "correlated logistic model with elastic net regularization for multilabel image classification"):
  with members:
    - "multi view intact space learning"
    - "video tracking using learned hierarchical features"
    - "return of the devil in the details delving deep into convolutional nets"
    - "matconvnet convolutional neural networks for matlab"
    - "multi label prediction via compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "distribution based label space transformation for multi label learning"):
  with members:
    - "learning spatial regularization with image level supervisions for multi label image classification"
    - "deep residual learning for image recognition"
    - "large scale multi label learning with missing labels"

Connected through hyperedge HE-4 (induced by source paper "learning deep latent spaces for multi label classification"):
  with members:
    - "hcp a flexible cnn framework for multi label image classification"
    - "cnn rnn a unified framework for multi label image classification"
    - "deep convolutional ranking for multilabel image annotation"
    - "large scale multi label learning with missing labels"
    - "the landmark selection method for multiple output prediction"
    - "multi label prediction via compressed sensing"

Connected through hyperedge HE-5 (induced by source paper "multi target regression via random linear target combinations"):
  with members:
    - "improving neural networks by preventing co adaptation of feature detectors"
    - "multi target regression via input space expansion treating targets as inputs"
    - "multi label classification methods for multi target regression"
    - "multi label prediction via compressed sensing"
    - "top down induction of clustering trees"

Connected through hyperedge HE-6 (induced by source paper "learning max margin tree predictors"):
  with members:
    - "block coordinate frank wolfe optimization for structural svms"
    - "structured sparsity inducing norms through submodular functions"

Connected through hyperedge HE-7 (induced by source paper "multi target regression via input space expansion treating targets as inputs"):
  with members:
    - "learning internal representations"
    - "rectifying classifier chains for multi label classification"
    - "multi target regression via random linear target combinations"
    - "efficient monte carlo methods for multi dimensional learning with classifier chains"
    - "the landmark selection method for multiple output prediction"
    - "multi label classification methods for multi target regression"
    - "a dirty model for multiple sparse regression"
    - "multi label prediction via compressed sensing"

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)