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: "multimodal sparse representation learning and applications"
  Abstract: Unsupervised methods have proven effective for discriminative tasks in a single-modality scenario. In this paper, we present a multimodal framework for learning sparse representations that can capture semantic correlation between modalities. The framework can model relationships at a higher level by forcing the shared sparse representation. In particular, we propose the use of joint dictionary learning technique for sparse coding and formulate the joint representation for concision, cross-modal representations (in case of a missing modality), and union of the cross-modal representations. Given the accelerated growth of multimodal data posted on the Web such as YouTube, Wikipedia, and Twitter, learning good multimodal features is becoming increasingly important. We show that the shared representations enabled by our framework substantially improve the classification performance under both unimodal and multimodal settings. We further show how deep architectures built on the proposed framework are effective for the case of highly nonlinear correlations between modalities. The effectiveness of our approach is demonstrated experimentally in image denoising, multimedia event detection and retrieval on the TRECVID dataset (audio-video), category classification on the Wikipedia dataset (image-text), and sentiment classification on PhotoTweet (image-text).

Connected through hyperedge HE-1 (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"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "correlational neural networks"
    - "stochastic optimization for deep cca via nonlinear orthogonal iterations"

Connected through hyperedge HE-2 (induced by source paper "distributed deep neural networks over the cloud the edge and end devices"):
  with members:
    - "embedded binarized neural networks"
    - "branchynet fast inference via early exiting from deep neural networks"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "spatially adaptive computation time for residual networks"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep residual learning for image recognition"
    - "firecaffe near linear acceleration of deep neural network training on compute clusters"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "generative adversarial networks for multimodal representation learning in video hyperlinking"):
  with members:
    - "invertible conditional gans for image editing"
    - "generative adversarial text to image synthesis"
    - "stackgan text to photo realistic image synthesis with stacked generative adversarial networks"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "conditional generative adversarial nets"

Connected through hyperedge HE-4 (induced by source paper "deep sparse coding for invariant multimodal halle berry neurons"):
  with members:
    - "dynamic routing between capsules"
    - "vqa visual question answering"
    - "deep residual learning for image recognition"
    - "imagenet large scale visual recognition challenge"
    - "unsupervised feature learning by deep sparse coding"
    - "building high level features using large scale unsupervised learning"

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)