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: "ways of conditioning generative adversarial networks"
  Abstract: The GANs are generative models whose random samples realistically reflect natural images. It also can generate samples with specific attributes by concatenating a condition vector into the input, yet research on this field is not well studied. We propose novel methods of conditioning generative adversarial networks (GANs) that achieve state-of-the-art results on MNIST and CIFAR-10. We mainly introduce two models: an information retrieving model that extracts conditional information from the samples, and a spatial bilinear pooling model that forms bilinear features derived from the spatial cross product of an image and a condition vector. These methods significantly enhance log-likelihood of test data under the conditional distributions compared to the methods of concatenation.

Connected through hyperedge HE-1 (induced by source paper "representation learning by rotating your faces"):
  with members:
    - "on learning 3d face morphable model from in the wild images"
    - "multi task convolutional neural network for pose invariant face recognition"
    - "additive margin softmax for face verification"
    - "nonlinear 3d face morphable model"
    - "began boundary equilibrium generative adversarial networks"
    - "pose invariant face alignment with a single cnn"
    - "fsrnet end to end learning face super resolution with facial priors"
    - "towards large pose face frontalization in the wild"

Connected through hyperedge HE-2 (induced by source paper "unpaired speech enhancement by acoustic and adversarial supervision for speech recognition"):
  with members:
    - "began boundary equilibrium generative adversarial networks"
    - "a network of deep neural networks for distant speech recognition"
    - "segan speech enhancement generative adversarial network"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "exploring speech enhancement with generative adversarial networks for robust speech recognition"
    - "a wavenet for speech denoising"

Connected through hyperedge HE-3 (induced by source paper "self paced adversarial training for multimodal few shot learning"):
  with members:
    - "self paced deep learning for weakly supervised object detection"
    - "chatpainter improving text to image generation using dialogue"
    - "low shot learning from imaginary data"
    - "low shot learning with large scale diffusion"
    - "a generative model for zero shot learning using conditional variational autoencoders"
    - "prototypical networks for few shot learning"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "vse improving visual semantic embeddings with hard negatives"

Connected through hyperedge HE-4 (induced by source paper "fast converging conditional generative adversarial networks for image synthesis"):
  with members:
    - "semi supervised fusedgan for conditional image generation"
    - "age progression regression by conditional adversarial autoencoder"
    - "neural photo editing with introspective adversarial networks"
    - "conditional image generation with pixelcnn decoders"
    - "learning what and where to draw"
    - "improved techniques for training gans"
    - "semantic image inpainting with deep generative models"
    - "image to image translation with conditional adversarial networks"

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)