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: "deep linear discriminant analysis"
  Abstract: We introduce Deep Linear Discriminant Analysis (DeepLDA) which learns linearly separable latent representations in an end-to-end fashion. Classic LDA extracts features which preserve class separability and is used for dimensionality reduction for many classification problems. The central idea of this paper is to put LDA on top of a deep neural network. This can be seen as a non-linear extension of classic LDA. Instead of maximizing the likelihood of target labels for individual samples, we propose an objective function that pushes the network to produce feature distributions which: (a) have low variance within the same class and (b) high variance between different classes. Our objective is derived from the general LDA eigenvalue problem and still allows to train with stochastic gradient descent and back-propagation. For evaluation we test our approach on three different benchmark datasets (MNIST, CIFAR-10 and STL-10). DeepLDA produces competitive results on MNIST and CIFAR-10 and outperforms a network trained with categorical cross entropy (same architecture) on a supervised setting of STL-10.

Connected through hyperedge HE-1 (induced by source paper "adaptive locality preserving regression"):
  with members:
    - "joint intermodal and intramodal label transfers for extremely rare or unseen classes"
    - "deep residual learning for image recognition"
    - "collaborative representation based classification for face recognition"

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"
    - "multimodal sparse representation learning and applications"
    - "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-3 (induced by source paper "multimodal representation learning using deep multiset canonical correlation"):
  with members:
    - "deep generalized canonical correlation analysis"
    - "robust subspace clustering"
    - "a kernel method for canonical correlation analysis"

Connected through hyperedge HE-4 (induced by source paper "generalized multi view embedding for visual recognition and cross modal retrieval"):
  with members:
    - "transductive multi view zero shot learning"
    - "skip thought vectors"
    - "aligning books and movies towards story like visual explanations by watching movies and reading books"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "a survey on multi view learning"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "semi supervised deep representation learning for multi view problems"):
  with members:
    - "deep generalized canonical correlation analysis"
    - "a comprehensive survey on cross modal retrieval"
    - "deep learning in neural networks"
    - "large scale approximate kernel canonical correlation analysis"
    - "adam a method for stochastic optimization"
    - "a survey on multi view learning"

Connected through hyperedge HE-6 (induced by source paper "icebreaker solving cold start problem for video recommendation engines"):
  with members:
    - "a multimodal approach to predict social media popularity"
    - "wide residual networks"
    - "wider or deeper revisiting the resnet model for visual recognition"
    - "adam a method for stochastic optimization"
    - "learning spatiotemporal features with 3d convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "simultaneously learning neighborship and projection matrix for supervised dimensionality reduction"):
  with members:
    - "vectorial dimension reduction for tensors based on bayesian inference"
    - "cascade learning by optimally partitioning"
    - "learning sampling distributions for efficient object detection"
    - "cascaded subpatch networks for effective cnns"
    - "convolution in convolution for network in network"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "from manifold to manifold geometry aware dimensionality reduction for spd matrices"

Connected through hyperedge HE-8 (induced by source paper "historical document image segmentation with lda initialized deep neural networks"):
  with members:
    - "open evaluation tool for layout analysis of document images"
    - "what you expect is not what you get questioning reconstruction classification correlation of stacked convolutional auto encoder features"
    - "pca initialized deep neural networks applied to document image analysis"
    - "deep learning in neural networks"
    - "data dependent initializations of convolutional neural networks"

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)