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: "disentangled variational auto encoder for semi supervised learning"
  Abstract: In this paper, we develop a novel approach for semi-supervised VAE without classifier. Specifically, we propose a new model called SDVAE, which encodes the input data into disentangled representation and non-interpretable representation, then the category information is directly utilized to regularize the disentangled representation via equation constraint. To further enhance the feature learning ability of the proposed VAE, we incorporate reinforcement learning to relieve the lack of data. The dynamic framework is capable of dealing with both image and text data with its corresponding encoder and decoder networks. Extensive experiments on image and text datasets demonstrate the effectiveness of the proposed framework.

Connected through hyperedge HE-1 (induced by source paper "latent space factorisation and manipulation via matrix subspace projection"):
  with members:
    - "evaluating the state of the art of end to end natural language generation the e2e nlg challenge"
    - "shaping the narrative arc an information theoretic approach to collaborative dialogue"
    - "deep nets what have they ever done for vision"
    - "learning latent subspaces in variational autoencoders"
    - "i revnet deep invertible networks"
    - "dirichlet variational autoencoder for text modeling"
    - "toward controlled generation of text"
    - "variational inference of disentangled latent concepts from unlabeled observations"

Connected through hyperedge HE-2 (induced by source paper "exploring semi supervised variational autoencoders for biomedical relation extraction"):
  with members:
    - "improved variational autoencoders for text modeling using dilated convolutions"
    - "variational autoencoders for semi supervised text classification"
    - "enriching word vectors with subword information"
    - "semi supervised sequence learning"
    - "semi supervised learning with deep generative models"
    - "efficient estimation of word representations in vector space"
    - "natural language processing almost from scratch"

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)