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: "correlational neural networks"
  Abstract: Common Representation Learning (CRL), wherein different descriptions (or views) of the data are embedded in a common subspace, is receiving a lot of attention recently. Two popular paradigms here are Canonical Correlation Analysis (CCA) based approaches and Autoencoder (AE) based approaches. CCA based approaches learn a joint representation by maximizing correlation of the views when projected to the common subspace. AE based methods learn a common representation by minimizing the error of reconstructing the two views. Each of these approaches has its own advantages and disadvantages. For example, while CCA based approaches outperform AE based approaches for the task of transfer learning, they are not as scalable as the latter. In this work we propose an AE based approach called Correlational Neural Network (CorrNet), that explicitly maximizes correlation among the views when projected to the common subspace. Through a series of experiments, we demonstrate that the proposed CorrNet is better than the above mentioned approaches with respect to its ability to learn correlated common representations. Further, we employ CorrNet for several cross language tasks and show that the representations learned using CorrNet perform better than the ones learned using other state of the art approaches.

Connected through hyperedge HE-1 (induced by source paper "towards a general model of knowledge for facial analysis by multi source transfer learning"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "delta deep learning transfer using feature map with attention for convolutional networks"
    - "mfas multimodal fusion architecture search"
    - "task2vec task embedding for meta learning"
    - "best sources forward domain generalization through source specific nets"
    - "measure manifold learning and optimization a theory of neural networks"
    - "deep clustering for unsupervised learning of visual features"
    - "taskonomy disentangling task transfer learning"

Connected through hyperedge HE-2 (induced by source paper "deep multi view learning using neuron wise correlation maximizing regularizers"):
  with members:
    - "dominant set clustering and pooling for multi view 3d object recognition"
    - "multi view intact space learning"
    - "depth cnns for rgb d scene recognition learning from scratch better than transferring from rgb cnns"
    - "theory of deep learning iib optimization properties of sgd"
    - "data dependent stability of stochastic gradient descent"
    - "aggregated residual transformations for deep neural networks"
    - "understanding deep learning requires rethinking generalization"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-3 (induced by source paper "learning multimodal representations for unseen activities"):
  with members:
    - "fine grained activity recognition in baseball videos"
    - "joint event detection and description in continuous video streams"
    - "semantic autoencoder for zero shot learning"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "zero shot learning via class conditioned deep generative models"
    - "dense captioning events in videos"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "learning similarity metrics for numerical simulations"):
  with members:
    - "data efficient image recognition with contrastive predictive coding"
    - "learning representations by maximizing mutual information across views"
    - "nima neural image assessment"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "siamese networks for semantic pattern similarity"
    - "pieapp perceptual image error assessment through pairwise preference"
    - "learned perceptual image enhancement"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-5 (induced by source paper "from bilingual to multilingual neural machine translation by incremental training"):
  with members:
    - "effective cross lingual transfer of neural machine translation models without shared vocabularies"
    - "multilingual multi scale and multi layer visualization of intermediate representations"
    - "a neural interlingua for multilingual machine translation"
    - "transfer learning in multilingual neural machine translation with dynamic vocabulary"
    - "learning joint multilingual sentence representations with neural machine translation"
    - "attention is all you need"
    - "unsupervised neural machine translation"
    - "zero resource translation with multi lingual neural machine translation"

Connected through hyperedge HE-6 (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"
    - "stochastic optimization for deep cca via nonlinear orthogonal iterations"

Connected through hyperedge HE-7 (induced by source paper "generalizing the convolution operator in convolutional neural networks"):
  with members:
    - "all you need is a good init"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "data dependent initializations of convolutional neural networks"
    - "going deeper with convolutions"
    - "imagenet large scale visual recognition challenge"
    - "caffe convolutional architecture for fast feature embedding"
    - "network in network"

Connected through hyperedge HE-8 (induced by source paper "canonical correlation analysis cca based multi view learning an overview"):
  with members:
    - "deep generalized canonical correlation analysis"
    - "multimodal machine learning a survey and taxonomy"
    - "deep variational canonical correlation analysis"
    - "an overview of cross media retrieval concepts methodologies benchmarks and challenges"
    - "a survey on multi view learning"
    - "manopt a matlab toolbox for optimization on manifolds"
    - "a kernel method for canonical correlation analysis"

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.CL(Computation and Language)