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: "stochastic optimization for deep cca via nonlinear orthogonal iterations"
  Abstract: Deep CCA is a recently proposed deep neural network extension to the traditional canonical correlation analysis (CCA), and has been successful for multi-view representation learning in several domains. However, stochastic optimization of the deep CCA objective is not straightforward, because it does not decouple over training examples. Previous optimizers for deep CCA are either batch-based algorithms or stochastic optimization using large minibatches, which can have high memory consumption. In this paper, we tackle the problem of stochastic optimization for deep CCA with small minibatches, based on an iterative solution to the CCA objective, and show that we can achieve as good performance as previous optimizers and thus alleviate the memory requirement.

Connected through hyperedge HE-1 (induced by source paper "a survey of multi view representation learning"):
  with members:
    - "word2visualvec cross media retrieval by visual feature prediction"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "efficient algorithms for large scale generalized eigenvector computation and canonical correlation analysis"
    - "rotationnet joint object categorization and pose estimation using multiviews from unsupervised viewpoints"
    - "globally convergent stochastic optimization for canonical correlation analysis"
    - "word2visualvec image and video to sentence matching by visual feature prediction"
    - "convolutional two stream network fusion for video action recognition"
    - "deep cross modal hashing"

Connected through hyperedge HE-2 (induced by source paper "deep multi view learning via task optimal cca"):
  with members:
    - "deep clustering for unsupervised learning of visual features"
    - "decorrelated batch normalization"
    - "end to end cross modality retrieval with cca projections and pairwise ranking loss"
    - "scalable and effective deep cca via soft decorrelation"
    - "deep linear discriminant analysis"
    - "multimodal sparse representation learning and applications"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "correlational neural networks"

Connected through hyperedge HE-3 (induced by source paper "quantifying the visual concreteness of words and topics in multimodal datasets"):
  with members:
    - "learning from noisy large scale datasets with minimal supervision"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "learning visual features from large weakly supervised data"
    - "microsoft coco captions data collection and evaluation server"
    - "combining language and vision with a multimodal skip gram model"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "an efficient approach to informative feature extraction from multimodal data"):
  with members:
    - "multimodal machine learning a survey and taxonomy"
    - "neural factorization machines for sparse predictive analytics"
    - "scalable and effective deep cca via soft decorrelation"
    - "deepfm a factorization machine based neural network for ctr prediction"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "correlational neural networks"
    - "order embeddings of images and language"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "mutual information maximization for simple and accurate part of speech induction"):
  with members:
    - "unsupervised learning of syntactic structure with invertible neural projections"
    - "representation learning with contrastive predictive coding"
    - "unsupervised neural hidden markov models"
    - "unsupervised pos induction with word embeddings"
    - "deep learning and the information bottleneck principle"
    - "conditional random field autoencoders for unsupervised structured prediction"

Connected through hyperedge HE-6 (induced by source paper "a unified framework for domain adaptation using metric learning on manifolds"):
  with members:
    - "return of frustratingly easy domain adaptation"
    - "optimal transport for domain adaptation"
    - "subspace alignment for domain adaptation"
    - "frustratingly easy domain adaptation"

Connected through hyperedge HE-7 (induced by source paper "scalable and effective deep cca via soft decorrelation"):
  with members:
    - "a discriminatively learned cnn embedding for person reidentification"
    - "re ranking person re identification with k reciprocal encoding"
    - "person re identification by deep joint learning of multi loss classification"
    - "hydraplus net attentive deep features for pedestrian analysis"
    - "end to end comparative attention networks for person re identification"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"
    - "learning deep context aware features over body and latent parts for person re identification"
    - "svdnet for pedestrian retrieval"

Connected through hyperedge HE-8 (induced by source paper "acoustic feature learning via deep variational canonical correlation analysis"):
  with members:
    - "deep variational canonical correlation analysis"
    - "multi view generative adversarial networks"
    - "representations of language in a model of visually grounded speech signal"
    - "energy based generative adversarial network"
    - "adversarial feature learning"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "adam a method for stochastic optimization"
    - "multilingual models for compositional distributed semantics"

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)