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: "associative compression networks for representation learning"
  Abstract: This paper introduces Associative Compression Networks (ACNs), a new framework for variational autoencoding with neural networks. The system differs from existing variational autoencoders (VAEs) in that the prior distribution used to model each code is conditioned on a similar code from the dataset. In compression terms this equates to sequentially transmitting the dataset using an ordering determined by proximity in latent space. Since the prior need only account for local, rather than global variations in the latent space, the coding cost is greatly reduced, leading to rich, informative codes. Crucially, the codes remain informative when powerful, autoregressive decoders are used, which we argue is fundamentally difficult with normal VAEs. Experimental results on MNIST, CIFAR-10, ImageNet and CelebA show that ACNs discover high-level latent features such as object class, writing style, pose and facial expression, which can be used to cluster and classify the data, as well as to generate diverse and convincing samples. We conclude that ACNs are a promising new direction for representation learning: one that steps away from IID modelling, and towards learning a structured description of the dataset as a whole.

Connected through hyperedge HE-1 (induced by source paper "large scale adversarial representation learning"):
  with members:
    - "tf replicator distributed machine learning for researchers"
    - "data efficient image recognition with contrastive predictive coding"
    - "revisiting self supervised visual representation learning"
    - "high fidelity image generation with fewer labels"
    - "self supervised gans via auxiliary rotation loss"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "unsupervised representation learning by predicting image rotations"

Connected through hyperedge HE-2 (induced by source paper "preventing posterior collapse with delta vaes"):
  with members:
    - "uncertainty in the variational information bottleneck"
    - "stochastic video generation with a learned prior"
    - "autoregressive quantile networks for generative modeling"
    - "spherical latent spaces for stable variational autoencoders"
    - "theory and experiments on vector quantized autoencoders"
    - "generating sentences by editing prototypes"
    - "attention is all you need"
    - "stochastic variational video prediction"

Connected through hyperedge HE-3 (induced by source paper "the functional neural process"):
  with members:
    - "functional variational bayesian neural networks"
    - "correlated variational auto encoders"
    - "attentive neural processes"
    - "eigenvalue corrected noisy natural gradient"
    - "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-4 (induced by source paper "implicit autoencoders"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "multimodal unsupervised image to image translation"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "fixing a broken elbo"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "conditional image generation with pixelcnn decoders"
    - "improved techniques for training gans"
    - "adversarial feature learning"

Connected through hyperedge HE-5 (induced by source paper "s trigger continual state representation learning via self triggered generative replay"):
  with members:
    - "s rl toolbox environments datasets and evaluation metrics for state representation learning"
    - "state representation learning for control an overview"
    - "life long disentangled representation learning with cross domain latent homologies"
    - "decoupling feature extraction from policy learning assessing benefits of state representation learning in goal based robotics"
    - "unsupervised predictive memory in a goal directed agent"
    - "flatland a lightweight first person 2 d environment for reinforcement learning"
    - "memory replay gans learning to generate images from new categories without forgetting"
    - "generative models from the perspective of continual learning"

Connected through hyperedge HE-6 (induced by source paper "the challenge of realistic music generation modelling raw audio at scale"):
  with members:
    - "generating wikipedia by summarizing long sequences"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "learning longer term dependencies in rnns with auxiliary losses"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "dilated recurrent neural networks"
    - "learning a hierarchical latent variable model of 3d shapes"
    - "deep learning techniques for music generation a survey"

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)