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: "incorporating characteristics of human creativity into an evolutionary art algorithm"
  Abstract: A perceived limitation of evolutionary art and design algorithms is that they rely on human intervention; the artist selects the most aesthetically pleasing variants of one generation to produce the next. This paper discusses how computer generated art and design can become more creatively human-like with respect to both process and outcome. As an example of a step in this direction, we present an algorithm that overcomes the above limitation by employing an automatic fitness function. The goal is to evolve abstract portraits of Darwin, using our 2nd generation fitness function which rewards genomes that not just produce a likeness of Darwin but exhibit certain strategies characteristic of human artists. We note that in human creativity, change is less choosing amongst randomly generated variants and more capitalizing on the associative structure of a conceptual network to hone in on a vision. We discuss how to achieve this fluidity algorithmically.

Connected through hyperedge HE-1 (induced by source paper "creativity inspired zero shot learning"):
  with members:
    - "design design inspiration from generative networks"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "toward a neuro inspired creative decoder"):
  with members:
    - "novelty detection with gan"
    - "inference suboptimality in variational autoencoders"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "can creative adversarial networks generating art by learning about styles and deviating from style norms"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "infovae information maximizing variational autoencoders"
    - "artgan artwork synthesis with conditional categorical gans"
    - "a neural representation of sketch drawings"

Connected through hyperedge HE-3 (induced by source paper "using an ai creativity system to explore how aesthetic experiences are processed along the brains perceptual neural pathways"):
  with members:
    - "informing artificial intelligence generative techniques using cognitive theories of human creativity"
    - "deep convolutional networks as models of generalization and blending within visual creativity"
    - "a neural algorithm of artistic style"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-4 (induced by source paper "design design inspiration from generative networks"):
  with members:
    - "texturegan controlling deep image synthesis with texture patches"
    - "a generative model of people in clothing"
    - "be your own prada fashion synthesis with structural coherence"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "deep learning techniques for music generation a survey"
    - "fashioning with networks neural style transfer to design clothes"
    - "a neural representation of sketch drawings"
    - "inception score label smoothing gradient vanishing and log d x alternative"

Connected through hyperedge HE-5 (induced by source paper "informing artificial intelligence generative techniques using cognitive theories of human creativity"):
  with members:
    - "deep convolutional networks as models of generalization and blending within visual creativity"
    - "generalizing prototype theory a formal quantum framework"
    - "deep generative image models using a laplacian pyramid of adversarial networks"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-6 (induced by source paper "arigan synthetic arabidopsis plants using generative adversarial network"):
  with members:
    - "end to end instance segmentation and counting with recurrent attention"
    - "end to end instance segmentation with recurrent attention"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "deep generative image models using a laplacian pyramid of adversarial networks"
    - "draw a recurrent neural network for image generation"
    - "recurrent instance segmentation"
    - "conditional generative adversarial nets"

Connected through hyperedge HE-7 (induced by source paper "can creative adversarial networks generating art by learning about styles and deviating from style norms"):
  with members:
    - "perceptual losses for real time style transfer and super resolution"
    - "generative adversarial text to image synthesis"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "quantifying creativity in art networks"
    - "empirical evaluation of rectified activations in convolutional network"

Connected through hyperedge HE-8 (induced by source paper "cultural evolution as distributed computation"):
  with members:
    - "society functions best with an intermediate level of creativity"
    - "a conceptual network based approach to inferring the cultural evolutionary history of the baltic psaltery"
    - "concepts and their dynamics a quantum theoretic modeling of human thought"
    - "meme and variations a computer model of cultural evolution"
    - "evoc a computer model of the evolution of culture"
    - "the guppy effect as interference"
    - "an agent based simulation of the effectiveness of creative leadership"
    - "how creative should creators be to optimize the evolution of ideas a computational model"

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.AI(Artificial Intelligence)