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: "generative class conditional autoencoders"
  Abstract: Recent work by Bengio et al. (2013) proposes a sampling procedure for denoising autoencoders which involves learning the transition operator of a Markov chain. The transition operator is typically unimodal, which limits its capacity to model complex data. In order to perform efficient sampling from conditional distributions, we extend this work, both theoretically and algorithmically, to gated autoencoders (Memisevic, 2013), The proposed model is able to generate convincing class-conditional samples when trained on both the MNIST and TFD datasets.

Connected through hyperedge HE-1 (induced by source paper "neurogenesis deep learning extending deep networks to accommodate new classes"):
  with members:
    - "deep learning in neural networks"
    - "draw a recurrent neural network for image generation"
    - "how transferable are features in deep neural networks"
    - "building high level features using large scale unsupervised learning"
    - "matrix inversion using cholesky decomposition"

Connected through hyperedge HE-2 (induced by source paper "gated networks an inventory"):
  with members:
    - "unsupervised learning of video representations using lstms"
    - "continuous control with deep reinforcement learning"
    - "modeling sequential data using higher order relational features and predictive training"
    - "mental rotation by optimizing transforming distance"
    - "deep learning of representations looking forward"
    - "on multi view feature learning"

Connected through hyperedge HE-3 (induced by source paper "censoring representations with an adversary"):
  with members:
    - "deep generative image models using a laplacian pyramid of adversarial networks"
    - "adam a method for stochastic optimization"
    - "screenavoider protecting computer screens from ubiquitous cameras"
    - "theano new features and speed improvements"

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.NE(Neural and Evolutionary Computing)