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: "latent constraints learning to generate conditionally from unconditional generative models"
  Abstract: Deep generative neural networks have proven effective at both conditional and unconditional modeling of complex data distributions. Conditional generation enables interactive control, but creating new controls often requires expensive retraining. In this paper, we develop a method to condition generation without retraining the model. By post-hoc learning latent constraints, value functions that identify regions in latent space that generate outputs with desired attributes, we can conditionally sample from these regions with gradient-based optimization or amortized actor functions. Combining attribute constraints with a universal "realism" constraint, which enforces similarity to the data distribution, we generate realistic conditional images from an unconditional variational autoencoder. Further, using gradient-based optimization, we demonstrate identity-preserving transformations that make the minimal adjustment in latent space to modify the attributes of an image. Finally, with discrete sequences of musical notes, we demonstrate zero-shot conditional generation, learning latent constraints in the absence of labeled data or a differentiable reward function. Code with dedicated cloud instance has been made publicly available (this https URL).

Connected through hyperedge HE-1 (induced by source paper "towards a controllable disentanglement network"):
  with members:
    - "soft gated warping gan for pose guided person image synthesis"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "isolating sources of disentanglement in variational autoencoders"
    - "variational inference of disentangled latent concepts from unlabeled observations"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "improved training of wasserstein gans"
    - "semantically decomposing the latent spaces of generative adversarial networks"
    - "stargan unified generative adversarial networks for multi domain image to image translation"

Connected through hyperedge HE-2 (induced by source paper "painting many pasts synthesizing time lapse videos of paintings"):
  with members:
    - "lpaintb learning to paint from self supervision"
    - "visual deprojection probabilistic recovery of collapsed dimensions"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "context aware synthesis for video frame interpolation"
    - "synthesizing programs for images using reinforced adversarial learning"
    - "learning to generate long term future via hierarchical prediction"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-3 (induced by source paper "controlling generative models with continuous factors of variations"):
  with members:
    - "generating diverse high fidelity images with vq vae 2"
    - "on the steerability of generative adversarial networks"
    - "ganalyze toward visual definitions of cognitive image properties"
    - "deeply supervised salient object detection with short connections"
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "inverting the generator of a generative adversarial network ii"
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-4 (induced by source paper "generative models for automatic chemical design"):
  with members:
    - "selfies a robust representation of semantically constrained graphs with an example application in chemistry"
    - "a model to search for synthesizable molecules"
    - "all smiles variational autoencoder"
    - "likelihood free inference and generation of molecular graphs"
    - "molecular geometry prediction using a deep generative graph neural network"
    - "labeled graph generative adversarial networks"
    - "decoding molecular graph embeddings with reinforcement learning"
    - "scaffold based molecular design using graph generative model"

Connected through hyperedge HE-5 (induced by source paper "dualdis dual branch disentangling with adversarial learning"):
  with members:
    - "a two step disentanglement method"
    - "learning disentangled representations with reference based variational autoencoders"
    - "all about structure adapting structural information across domains for boosting semantic segmentation"
    - "label propagation for deep semi supervised learning"
    - "dual swap disentangling"
    - "hybridnet classification and reconstruction cooperation for semi supervised learning"
    - "exploring disentangled feature representation beyond face identification"
    - "isolating sources of disentanglement in variational autoencoders"

Connected through hyperedge HE-6 (induced by source paper "learning generative models across incomparable spaces"):
  with members:
    - "gromov wasserstein alignment of word embedding spaces"
    - "the gromov wasserstein distance between networks and stable network invariants"
    - "improving gans using optimal transport"
    - "large scale gan training for high fidelity natural image synthesis"
    - "the cramer distance as a solution to biased wasserstein gradients"
    - "mcgan mean and covariance feature matching gan"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "emnist an extension of mnist to handwritten letters"

Connected through hyperedge HE-7 (induced by source paper "latent translation crossing modalities by bridging generative models"):
  with members:
    - "gansynth adversarial neural audio synthesis"
    - "video to video synthesis"
    - "twin gan unpaired cross domain image translation with weight sharing gans"
    - "adversarial audio synthesis"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "a universal music translation network"
    - "generative modeling using the sliced wasserstein distance"
    - "phrase based neural unsupervised machine translation"

Connected through hyperedge HE-8 (induced by source paper "learning variations in human motion via mix and match perturbation"):
  with members:
    - "action anticipation with rbf kernelized feature mapping rnn"
    - "videoflow a flow based generative model for video"
    - "modeling human motion with quaternion based neural networks"
    - "human motion modeling using dvgans"
    - "bihmp gan bidirectional 3d human motion prediction gan"
    - "quaternet a quaternion based recurrent model for human motion"
    - "mt vae learning motion transformations to generate multimodal human dynamics"
    - "viena2 a driving anticipation dataset"

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)