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: "design design inspiration from generative networks"
  Abstract: Can an algorithm create original and compelling fashion designs to serve as an inspirational assistant? To help answer this question, we design and investigate different image generation models associated with different loss functions to boost creativity in fashion generation. The dimensions of our explorations include: (i) different Generative Adversarial Networks architectures that start from noise vectors to generate fashion items, (ii) a new loss function that encourages creativity, and (iii) a generation process following the key elements of fashion design (disentangling shape and texture makers). A key challenge of this study is the evaluation of generated designs and the retrieval of best ones, hence we put together an evaluation protocol associating automatic metrics and human experimental studies that we hope will help ease future research. We show that our proposed creativity loss yields better overall appreciation than the one employed in Creative Adversarial Networks. In the end, about 61% of our images are thought to be created by human designers rather than by a computer while also being considered original per our human subject experiments, and our proposed loss scores the highest compared to existing losses in both novelty and likability.

Connected through hyperedge HE-1 (induced by source paper "machine learning based co creative design framework"):
  with members:
    - "friend collaborator student manager how design of an ai driven game level editor affects creators"
    - "toward a neuro inspired creative decoder"
    - "co creative level design via machine learning"
    - "outfit generation and style extraction via bidirectional lstm and autoencoder"
    - "styling with attention to details"
    - "generative image inpainting with contextual attention"
    - "can creative adversarial networks generating art by learning about styles and deviating from style norms"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-2 (induced by source paper "compatible and diverse fashion image inpainting"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"
    - "learning type aware embeddings for fashion compatibility"
    - "instance level human parsing via part grouping network"
    - "toward characteristic preserving image based virtual try on network"
    - "multimodal unsupervised image to image translation"
    - "language guided fashion image manipulation with feature wise transformations"
    - "a variational u net for conditional appearance and shape generation"

Connected through hyperedge HE-3 (induced by source paper "creativity inspired zero shot learning"):
  with members:
    - "multi modal cycle consistent generalized zero shot learning"
    - "semantic autoencoder for zero shot learning"
    - "can creative adversarial networks generating art by learning about styles and deviating from style norms"
    - "link the head to the beak zero shot learning from noisy text description at part precision"
    - "learning robust visual semantic embeddings"
    - "from zero shot learning to conventional supervised classification unseen visual data synthesis"
    - "a generative adversarial approach for zero shot learning from noisy texts"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-4 (induced by source paper "generating interactive worlds with text"):
  with members:
    - "real time inference in multi sentence tasks with deep pretrained transformers"
    - "learning to speak and act in a fantasy text adventure game"
    - "gender bias in contextualized word embeddings"
    - "what makes a good conversation how controllable attributes affect human judgments"
    - "understanding the origins of bias in word embeddings"
    - "hierarchical neural story generation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "playing text adventure games with graph based deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "design and evaluation of product aesthetics a human machine hybrid approach"):
  with members:
    - "3d shape synthesis for conceptual design and optimization using variational autoencoders"
    - "towards photographic image manipulation with balanced growing of generative autoencoders"
    - "introvae introspective variational autoencoders for photographic image synthesis"
    - "learning type aware embeddings for fashion compatibility"
    - "pioneer networks progressively growing generative autoencoder"
    - "spectral normalization for generative adversarial networks"
    - "began boundary equilibrium generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-6 (induced by source paper "generative creativity adversarial learning for bionic design"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "multimodal unsupervised image to image translation"
    - "deep painterly harmonization"
    - "multi agent diverse generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "semantic image synthesis via adversarial learning"
    - "pixelnn example based image synthesis"
    - "arbitrary style transfer in real time with adaptive instance normalization"

Connected through hyperedge HE-7 (induced by source paper "rethinking monocular depth estimation with adversarial training"):
  with members:
    - "unsupervised adversarial depth estimation using cycled generative networks"
    - "cross view image synthesis using conditional gans"
    - "deep ordinal regression network for monocular depth estimation"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "discrimnet semi supervised action recognition from videos using generative adversarial networks"
    - "spectral normalization for generative adversarial networks"
    - "monocular depth estimation using multi scale continuous crfs as sequential deep networks"
    - "structured attention guided convolutional neural fields for monocular depth estimation"

Connected through hyperedge HE-8 (induced by source paper "vector image generation by learning parametric layer decomposition"):
  with members:
    - "the gan landscape losses architectures regularization and normalization"
    - "group normalization"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "compositional gan learning conditional image composition"
    - "spectral normalization for generative adversarial networks"
    - "atlasnet a papier m ach e approach to learning 3d surface generation"
    - "large scale gan training for high fidelity natural image synthesis"
    - "gan based synthetic medical image augmentation for increased cnn performance in liver lesion classification"

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)