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: "escaping from collapsing modes in a constrained space"
  Abstract: Generative adversarial networks (GANs) often suffer from unpredictable mode-collapsing during training. We study the issue of mode collapse of Boundary Equilibrium Generative Adversarial Network (BEGAN), which is one of the state-of-the-art generative models. Despite its potential of generating high-quality images, we find that BEGAN tends to collapse at some modes after a period of training. We propose a new model, called \emph{BEGAN with a Constrained Space} (BEGAN-CS), which includes a latent-space constraint in the loss function. We show that BEGAN-CS can significantly improve training stability and suppress mode collapse without either increasing the model complexity or degrading the image quality. Further, we visualize the distribution of latent vectors to elucidate the effect of latent-space constraint. The experimental results show that our method has additional advantages of being able to train on small datasets and to generate images similar to a given real image yet with variations of designated attributes on-the-fly.

Connected through hyperedge HE-1 (induced by source paper "simple and effective prevention of mode collapse in deep one class classification"):
  with members:
    - "deep semi supervised anomaly detection"
    - "robust anomaly detection in images using adversarial autoencoders"
    - "efficient gan based anomaly detection"
    - "deep anomaly detection using geometric transformations"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "began boundary equilibrium generative adversarial networks"
    - "noisy softmax improving the generalization ability of dcnn via postponing the early softmax saturation"

Connected through hyperedge HE-2 (induced by source paper "meshgan non linear 3d morphable models of faces"):
  with members:
    - "generating 3d faces using convolutional mesh autoencoders"
    - "inverting the generator of a generative adversarial network ii"
    - "relational inductive biases deep learning and graph networks"
    - "a simple approach to intrinsic correspondence learning on unstructured 3d meshes"
    - "motifnet a motif based graph convolutional network for directed graphs"
    - "nonlinear 3d face morphable model"
    - "variational autoencoders for deforming 3d mesh models"
    - "began boundary equilibrium generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "coco gan generation by parts via conditional coordinating"):
  with members:
    - "horizonnet learning room layout with 1d representation and pano stretch data augmentation"
    - "cgans with projection discriminator"
    - "introvae introspective variational autoencoders for photographic image synthesis"
    - "cube padding for weakly supervised saliency prediction in 360 deg videos"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "spectral normalization for generative adversarial networks"
    - "painting outside the box image outpainting with gans"
    - "which training methods for gans do actually converge"

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)