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: "contextual rnn gans for abstract reasoning diagram generation"
  Abstract: Understanding, predicting, and generating object motions and transformations is a core problem in artificial intelligence. Modeling sequences of evolving images may provide better representations and models of motion and may ultimately be used for forecasting, simulation, or video generation. Diagrammatic Abstract Reasoning is an avenue in which diagrams evolve in complex patterns and one needs to infer the underlying pattern sequence and generate the next image in the sequence. For this, we develop a novel Contextual Generative Adversarial Network based on Recurrent Neural Networks (Context-RNN-GANs), where both the generator and the discriminator modules are based on contextual history (modeled as RNNs) and the adversarial discriminator guides the generator to produce realistic images for the particular time step in the image sequence. We evaluate the Context-RNN-GAN model (and its variants) on a novel dataset of Diagrammatic Abstract Reasoning, where it performs competitively with 10th-grade human performance but there is still scope for interesting improvements as compared to college-grade human performance. We also evaluate our model on a standard video next-frame prediction task, achieving improved performance over comparable state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "learning to decompose and disentangle representations for video prediction"):
  with members:
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "sequential attend infer repeat generative modelling of moving objects"
    - "visual robot task planning"
    - "decomposing motion and content for natural video sequence prediction"
    - "learning to generate long term future via hierarchical prediction"
    - "folded recurrent neural networks for future video prediction"
    - "unsupervised learning of disentangled representations from video"
    - "neural expectation maximization"

Connected through hyperedge HE-2 (induced by source paper "dilated temporal relational adversarial network for generic video summarization"):
  with members:
    - "viewpoint aware video summarization"
    - "how local is the local diversity reinforcing sequential determinantal point processes with dynamic ground sets for supervised video summarization"
    - "dual motion gan for future flow embedded video prediction"
    - "deep reinforcement learning for unsupervised video summarization with diversity representativeness reward"
    - "video summarization with attention based encoder decoder networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "collaborative summarization of topic related videos"
    - "rethinking atrous convolution for semantic image segmentation"

Connected through hyperedge HE-3 (induced by source paper "c4synth cross caption cycle consistent text to image synthesis"):
  with members:
    - "chatpainter improving text to image generation using dialogue"
    - "translating and segmenting multimodal medical volumes with cycle and shape consistency generative adversarial network"
    - "photographic text to image synthesis with a hierarchically nested adversarial network"
    - "the neural painter multi turn image generation"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "sampling free uncertainty estimation in gated recurrent units with exponential families"):
  with members:
    - "lstm a search space odyssey"
    - "bayesian recurrent neural networks"
    - "natural parameter networks a class of probabilistic neural networks"
    - "video ladder networks"
    - "exploring the limits of language modeling"
    - "video pixel networks"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "deep speech 2 end to end speech recognition in english and mandarin"

Connected through hyperedge HE-5 (induced by source paper "structured set matching networks for one shot part labeling"):
  with members:
    - "scnet learning semantic correspondence"
    - "sequence to sequence learning as beam search optimization"
    - "a diagram is worth a dozen images"
    - "globally normalized transition based neural networks"
    - "probabilistic neural programs"
    - "semantic parsing to probabilistic programs for situated question answering"
    - "matching networks for one shot learning"
    - "computing the stereo matching cost with a convolutional neural network"

Connected through hyperedge HE-6 (induced by source paper "multi agent diverse generative adversarial networks"):
  with members:
    - "learning to protect communications with adversarial neural cryptography"
    - "began boundary equilibrium generative adversarial networks"
    - "gang of gans generative adversarial networks with maximum margin ranking"
    - "generalization and equilibrium in generative adversarial nets gans"
    - "generative multi adversarial networks"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "message passing multi agent gans"
    - "unrolled generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "wasserstein learning of deep generative point process models"):
  with members:
    - "improved training of wasserstein gans"
    - "nips 2016 tutorial generative adversarial networks"
    - "plug play generative networks conditional iterative generation of images in latent space"
    - "c rnn gan continuous recurrent neural networks with adversarial training"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "message passing multi agent gans"):
  with members:
    - "learning to protect communications with adversarial neural cryptography"
    - "generative multi adversarial networks"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "stochastic multiple choice learning for training diverse deep ensembles"
    - "improved techniques for training gans"
    - "generative adversarial text to image synthesis"
    - "coupled generative adversarial networks"
    - "context encoders feature learning by inpainting"

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.CV(Computer Vision and Pattern Recognition)