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 generative models with learnable knowledge constraints"
  Abstract: The broad set of deep generative models (DGMs) has achieved remarkable advances. However, it is often difficult to incorporate rich structured domain knowledge with the end-to-end DGMs. Posterior regularization (PR) offers a principled framework to impose structured constraints on probabilistic models, but has limited applicability to the diverse DGMs that can lack a Bayesian formulation or even explicit density evaluation. PR also requires constraints to be fully specified a priori, which is impractical or suboptimal for complex knowledge with learnable uncertain parts. In this paper, we establish mathematical correspondence between PR and reinforcement learning (RL), and, based on the connection, expand PR to learn constraints as the extrinsic reward in RL. The resulting algorithm is model-agnostic to apply to any DGMs, and is flexible to adapt arbitrary constraints with the model jointly. Experiments on human image generation and templated sentence generation show models with learned knowledge constraints by our algorithm greatly improve over base generative models.

Connected through hyperedge HE-1 (induced by source paper "deep reasoning networks thinking fast and slow"):
  with members:
    - "pdp a general neural framework for learning constraint satisfaction solvers"
    - "a pca like autoencoder"
    - "melding the data decisions pipeline decision focused learning for combinatorial optimization"
    - "learning a sat solver from single bit supervision"
    - "machine learning for combinatorial optimization a methodological tour d horizon"
    - "recurrent relational networks for complex relational reasoning"
    - "combinatorial optimization with graph convolutional networks and guided tree search"
    - "end to end learning for the deep multivariate probit model"

Connected through hyperedge HE-2 (induced by source paper "texar a modularized versatile and extensible toolkit for text generation"):
  with members:
    - "toward unsupervised text content manipulation"
    - "target guided open domain conversation"
    - "deepmind control suite"
    - "unsupervised text style transfer using language models as discriminators"
    - "tensor2tensor for neural machine translation"
    - "xnmt the extensible neural machine translation toolkit"
    - "marian fast neural machine translation in c"
    - "hybrid retrieval generation reinforced agent for medical image report generation"

Connected through hyperedge HE-3 (induced by source paper "unsupervised text style transfer using language models as discriminators"):
  with members:
    - "texar a modularized versatile and extensible toolkit for text generation"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "style transfer through back translation"
    - "unsupervised cipher cracking using discrete gans"
    - "toward controlled generation of text"
    - "style transfer from non parallel text by cross alignment"
    - "unsupervised machine translation using monolingual corpora only"
    - "improved variational autoencoders for text modeling using dilated convolutions"

Connected through hyperedge HE-4 (induced by source paper "image based model parameter optimisation using model assisted generative adversarial networks"):
  with members:
    - "face aging with conditional generative adversarial networks"
    - "improved techniques for training gans"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "tensorflow a system for large scale machine learning"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "facenet a unified embedding for face recognition and clustering"
    - "adam a method for stochastic optimization"
    - "conditional generative adversarial nets"

Connected through hyperedge HE-5 (induced by source paper "multi view factorization autoencoder with network constraints for multi omic integrative analysis"):
  with members:
    - "multimodal machine learning a survey and taxonomy"
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

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)