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 generalized canonical correlation analysis"
  Abstract: We present Deep Generalized Canonical Correlation Analysis (DGCCA) -- a method for learning nonlinear transformations of arbitrarily many views of data, such that the resulting transformations are maximally informative of each other. While methods for nonlinear two-view representation learning (Deep CCA, (Andrew et al., 2013)) and linear many-view representation learning (Generalized CCA (Horst, 1961)) exist, DGCCA is the first CCA-style multiview representation learning technique that combines the flexibility of nonlinear (deep) representation learning with the statistical power of incorporating information from many independent sources, or views. We present the DGCCA formulation as well as an efficient stochastic optimization algorithm for solving it. We learn DGCCA representations on two distinct datasets for three downstream tasks: phonetic transcription from acoustic and articulatory measurements, and recommending hashtags and friends on a dataset of Twitter users. We find that DGCCA representations soundly beat existing methods at phonetic transcription and hashtag recommendation, and in general perform no worse than standard linear many-view techniques.

Connected through hyperedge HE-1 (induced by source paper "on universal features for high dimensional learning and inference"):
  with members:
    - "on the sample complexity of hgr maximal correlation functions"
    - "probabilistic clustering using maximal matrix norm couplings"
    - "an information theoretic framework for non linear canonical correlation analysis"
    - "strong data processing inequalities for channels and bayesian networks"
    - "robust subspace learning robust pca robust subspace tracking and robust subspace recovery"
    - "principal inertia components and applications"
    - "opening the black box of deep neural networks via information"
    - "on the emergence of invariance and disentangling in deep representations"

Connected through hyperedge HE-2 (induced by source paper "canonical correlation analysis cca based multi view learning an overview"):
  with members:
    - "multimodal machine learning a survey and taxonomy"
    - "deep variational canonical correlation analysis"
    - "an overview of cross media retrieval concepts methodologies benchmarks and challenges"
    - "correlational neural networks"
    - "a survey on multi view learning"
    - "manopt a matlab toolbox for optimization on manifolds"
    - "a kernel method for canonical correlation analysis"

Connected through hyperedge HE-3 (induced by source paper "neural network assisted nonlinear multiview component analysis identifiability and algorithm"):
  with members:
    - "structured sumcor multiview canonical correlation analysis for large scale data"
    - "learning nonlinear mixtures identifiability and algorithm"
    - "on identifiability of nonnegative matrix factorization"
    - "efficient globally convergent stochastic optimization for canonical correlation analysis"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "multiview representation learning for a union of subspaces"):
  with members:
    - "learning from multiview correlations in open domain videos"
    - "six challenges for neural machine translation"
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"

Connected through hyperedge HE-5 (induced by source paper "multimodal representation learning using deep multiset canonical correlation"):
  with members:
    - "deep linear discriminant analysis"
    - "robust subspace clustering"
    - "a kernel method for canonical correlation analysis"

Connected through hyperedge HE-6 (induced by source paper "learning representations of social media users"):
  with members:
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "what s ur type contextualized classification of user types in marijuana related communications using compositional multiview embedding"
    - "multi task learning for argumentation mining in low resource settings"
    - "semi supervised user geolocation via graph convolutional networks"
    - "cascade contextual sarcasm detection in online discussion forums"
    - "identifying beneficial task relations for multi task learning in deep neural networks"
    - "tweet2vec learning tweet embeddings using character level cnn lstm encoder decoder"
    - "modelling context with user embeddings for sarcasm detection in social media"

Connected through hyperedge HE-7 (induced by source paper "learning from multiview correlations in open domain videos"):
  with members:
    - "unsupervised cross modal alignment of speech and text embedding spaces"
    - "how2 a large scale dataset for multimodal language understanding"
    - "learning joint multilingual sentence representations with neural machine translation"
    - "learning word like units from joint audio visual analysis"
    - "objects that sound"
    - "multimodal machine learning a survey and taxonomy"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "deep multimodal representation learning from temporal data"

Connected through hyperedge HE-8 (induced by source paper "correlated embeddings of pairs of dependent random variables"):
  with members:
    - "on the sample complexity of hgr maximal correlation functions"
    - "a survey of multi view representation learning"
    - "an efficient approach to informative feature extraction from multimodal data"
    - "a generalized power iteration method for solving quadratic problem on the stiefel manifold"
    - "deep variational canonical correlation analysis"
    - "principal inertia components and applications"
    - "tensorflow a system for large scale machine learning"
    - "linear bounds between contraction coefficients for f divergences"

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)