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: "a distributed approach toward discriminative distance metric learning"
  Abstract: Distance metric learning (DML) is successful in discovering intrinsic relations in data. However, most algorithms are computationally demanding when the problem size becomes large. In this paper, we propose a discriminative metric learning algorithm, develop a distributed scheme learning metrics on moderate-sized subsets of data, and aggregate the results into a global solution. The technique leverages the power of parallel computation. The algorithm of the aggregated DML (ADML) scales well with the data size and can be controlled by the partition. We theoretically analyze and provide bounds for the error induced by the distributed treatment. We have conducted experimental evaluation of the ADML, both on specially designed tests and on practical image annotation tasks. Those tests have shown that the ADML achieves the state-of-the-art performance at only a fraction of the cost incurred by most existing methods.

Connected through hyperedge HE-1 (induced by source paper "learning structured semantic embeddings for visual recognition"):
  with members:
    - "local similarity aware deep feature embedding"
    - "an empirical study and analysis of generalized zero shot learning for object recognition in the wild"
    - "multi instance visual semantic embedding"
    - "learning deep structure preserving image text embeddings"
    - "unsupervised learning of visual representations using videos"
    - "facenet a unified embedding for face recognition and clustering"
    - "embedding label structures for fine grained feature representation"
    - "visual word2vec vis w2v learning visually grounded word embeddings using abstract scenes"

Connected through hyperedge HE-2 (induced by source paper "cnn rnn a unified framework for multi label image classification"):
  with members:
    - "hcp a flexible cnn framework for multi label image classification"
    - "compositional distributional semantics with long short term memory"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "deep captioning with multimodal recurrent neural networks m rnn"
    - "caffe convolutional architecture for fast feature embedding"
    - "microsoft coco common objects in context"
    - "cnn features off the shelf an astounding baseline for recognition"

Connected through hyperedge HE-3 (induced by source paper "bayesian neighbourhood component analysis"):
  with members:
    - "unsupervised feature learning with c svddnet"
    - "bayesian active distance metric learning"

Connected through hyperedge HE-4 (induced by source paper "elm based distributed cooperative learning over networks"):
  with members:
    - "incremental gradient subgradient and proximal methods for convex optimization a survey"
    - "big learning with bayesian methods"
    - "zero gradient sum algorithms for distributed convex optimization the continuous time case"
    - "graphlab a new framework for parallel machine learning"
    - "sensor networks with random links topology design for distributed consensus"

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)