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: "exemplar centered supervised shallow parametric data embedding"
  Abstract: Metric learning methods for dimensionality reduction in combination with k-Nearest Neighbors (kNN) have been extensively deployed in many classification, data embedding, and information retrieval applications. However, most of these approaches involve pairwise training data comparisons, and thus have quadratic computational complexity with respect to the size of training set, preventing them from scaling to fairly big datasets. Moreover, during testing, comparing test data against all the training data points is also expensive in terms of both computational cost and resources required. Furthermore, previous metrics are either too constrained or too expressive to be well learned. To effectively solve these issues, we present an exemplar-centered supervised shallow parametric data embedding model, using a Maximally Collapsing Metric Learning (MCML) objective. Our strategy learns a shallow high-order parametric embedding function and compares training/test data only with learned or precomputed exemplars, resulting in a cost function with linear computational complexity for both training and testing. We also empirically demonstrate, using several benchmark datasets, that for classification in two-dimensional embedding space, our approach not only gains speedup of kNN by hundreds of times, but also outperforms state-of-the-art supervised embedding approaches.

Connected through hyperedge HE-1 (induced by source paper "supervised dimensionality reduction and visualization using centroid encoder"):
  with members:
    - "deeppainter painter classification using deep convolutional autoencoders"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "deep texture manifold for ground terrain recognition"):
  with members:
    - "island loss for learning discriminative features in facial expression recognition"
    - "mdnet a semantically and visually interpretable medical image diagnosis network"
    - "hidden two stream convolutional networks for action recognition"
    - "generative adversarial network based synthesis of visible faces from polarimetric thermal faces"
    - "a recurrent encoder decoder network for sequential face alignment"
    - "deep ten texture encoding network"
    - "a 4d light field dataset and cnn architectures for material recognition"
    - "depth2action exploring embedded depth for large scale action recognition"

Connected through hyperedge HE-3 (induced by source paper "parametric t distributed stochastic exemplar centered embedding"):
  with members:
    - "a deep learning model for structured outputs with high order interaction"
    - "barnes hut sne"
    - "partial hessian strategies for fast learning of nonlinear embeddings"
    - "scalable k means"

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)