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: "correlated variational auto encoders"
  Abstract: Variational Auto-Encoders (VAEs) are capable of learning latent representations for high dimensional data. However, due to the i.i.d. assumption, VAEs only optimize the singleton variational distributions and fail to account for the correlations between data points, which might be crucial for learning latent representations from dataset where a priori we know correlations exist. We propose Correlated Variational Auto-Encoders (CVAEs) that can take the correlation structure into consideration when learning latent representations with VAEs. CVAEs apply a prior based on the correlation structure. To address the intractability introduced by the correlated prior, we develop an approximation by average of a set of tractable lower bounds over all maximal acyclic subgraphs of the undirected correlation graph. Experimental results on matching and link prediction on public benchmark rating datasets and spectral clustering on a synthetic dataset show the effectiveness of the proposed method over baseline algorithms.

Connected through hyperedge HE-1 (induced by source paper "on the equivalence between node embeddings and structural graph representations"):
  with members:
    - "from community to role based graph embeddings"
    - "graph representation ensemble learning"
    - "is a single embedding enough learning node representations that capture multiple social contexts"
    - "on the limitations of representing functions on sets"
    - "position aware graph neural networks"
    - "what can neural networks reason about"
    - "on the equivalence between graph isomorphism testing and function approximation with gnns"
    - "a comprehensive survey on graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "the functional neural process"):
  with members:
    - "functional variational bayesian neural networks"
    - "attentive neural processes"
    - "eigenvalue corrected noisy natural gradient"
    - "associative compression networks for representation learning"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "noisy natural gradient as variational inference"
    - "prototypical networks for few shot learning"
    - "learning to compare relation network for few shot learning"

Connected through hyperedge HE-3 (induced by source paper "learning correlated latent representations with adaptive priors"):
  with members:
    - "structvae tree structured latent variable models for semi supervised semantic parsing"
    - "learning convolutional neural networks for graphs"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "tensorflow a system for large scale machine learning"
    - "convolutional networks on graphs for learning molecular fingerprints"
    - "adam a method for stochastic optimization"
    - "spectral networks and locally connected networks on graphs"
    - "a new class of upper bounds on the log partition function"

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)