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: "the landmark selection method for multiple output prediction"
  Abstract: Conditional modeling x \to y is a central problem in machine learning. A substantial research effort is devoted to such modeling when x is high dimensional. We consider, instead, the case of a high dimensional y, where x is either low dimensional or high dimensional. Our approach is based on selecting a small subset y_L of the dimensions of y, and proceed by modeling (i) x \to y_L and (ii) y_L \to y. Composing these two models, we obtain a conditional model x \to y that possesses convenient statistical properties. Multi-label classification and multivariate regression experiments on several datasets show that this model outperforms the one vs. all approach as well as several sophisticated multiple output prediction methods.

Connected through hyperedge HE-1 (induced by source paper "towards scalable and reliable capsule networks for challenging nlp applications"):
  with members:
    - "text processing like humans do visually attacking and shielding nlp systems"
    - "achieving human parity on automatic chinese to english news translation"
    - "fast dynamic routing based on weighted kernel density estimation"
    - "zero shot user intent detection via capsule neural networks"
    - "towards linear time neural machine translation with capsule networks"
    - "learning to rank question answer pairs with holographic dual lstm architecture"
    - "dynamic routing between capsules"
    - "attentive pooling networks"

Connected through hyperedge HE-2 (induced by source paper "label aware document representation via hybrid attention for extreme multi label text classification"):
  with members:
    - "attentionxml extreme multi label text classification with multi label attention based recurrent neural networks"
    - "sgm sequence generation model for multi label classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "explicit interaction model towards text classification"
    - "deep extreme multi label learning"
    - "a structured self attentive sentence embedding"
    - "node2vec scalable feature learning for networks"
    - "text classification improved by integrating bidirectional lstm with two dimensional max pooling"

Connected through hyperedge HE-3 (induced by source paper "ranking based autoencoder for extreme multi label classification"):
  with members:
    - "cbam convolutional block attention module"
    - "squeeze and excitation networks"
    - "on extending neural networks with loss ensembles for text classification"
    - "a structured self attentive sentence embedding"
    - "effective use of word order for text categorization with convolutional neural networks"
    - "convolutional neural networks for sentence classification"
    - "training deep neural networks on noisy labels with bootstrapping"
    - "large scale multi label learning with missing labels"

Connected through hyperedge HE-4 (induced by source paper "collaborative filtering and multi label classification with matrix factorization"):
  with members:
    - "cnn rnn a unified framework for multi label image classification"
    - "robust low rank matrix factorization under general mixture noise distributions"
    - "scalable multilabel prediction via randomized methods"
    - "large scale multi label learning with missing labels"
    - "bpr bayesian personalized ranking from implicit feedback"
    - "multi label prediction via compressed sensing"
    - "a tutorial on spectral clustering"
    - "non negative matrix factorization with sparseness constraints"

Connected through hyperedge HE-5 (induced by source paper "ldsm logarithm depth streaming multi label decision trees"):
  with members:
    - "bonsai diverse and shallow trees for extreme multi label classification"
    - "a no regret generalization of hierarchical softmax to extreme multi label classification"
    - "deep extreme multi label learning"
    - "logarithmic time online multiclass prediction"
    - "normalized online learning"
    - "large scale multi label learning with missing labels"
    - "multi label prediction via compressed sensing"

Connected through hyperedge HE-6 (induced by source paper "dynamic principal projection for cost sensitive online multi label classification"):
  with members:
    - "large scale multi label learning with missing labels"
    - "multi label prediction via compressed sensing"

Connected through hyperedge HE-7 (induced by source paper "group preserving label embedding for multi label classification"):
  with members:
    - "scalable multilabel prediction via randomized methods"
    - "large scale multi label learning with missing labels"
    - "multi label prediction via compressed sensing"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-8 (induced by source paper "online multi label classification a label compression method"):
  with members:
    - "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)