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: "face aging with contextual generative adversarial nets"
  Abstract: Face aging, which renders aging faces for an input face, has attracted extensive attention in the multimedia research. Recently, several conditional Generative Adversarial Nets (GANs) based methods have achieved great success. They can generate images fitting the real face distributions conditioned on each individual age group. However, these methods fail to capture the transition patterns, e.g., the gradual shape and texture changes between adjacent age groups. In this paper, we propose a novel Contextual Generative Adversarial Nets (C-GANs) to specifically take it into consideration. The C-GANs consists of a conditional transformation network and two discriminative networks. The conditional transformation network imitates the aging procedure with several specially designed residual blocks. The age discriminative network guides the synthesized face to fit the real conditional distribution. The transition pattern discriminative network is novel, aiming to distinguish the real transition patterns with the fake ones. It serves as an extra regularization term for the conditional transformation network, ensuring the generated image pairs to fit the corresponding real transition pattern distribution. Experimental results demonstrate the proposed framework produces appealing results by comparing with the state-of-the-art and ground truth. We also observe performance gain for cross-age face verification.

Connected through hyperedge HE-1 (induced by source paper "ugan untraceable gan for multi domain face translation"):
  with members:
    - "cgans with projection discriminator"
    - "the relativistic discriminator a key element missing from standard gan"
    - "3d aware scene manipulation via inverse graphics"
    - "modular generative adversarial networks"
    - "spectral normalization for generative adversarial networks"
    - "a variational u net for conditional appearance and shape generation"
    - "large scale gan training for high fidelity natural image synthesis"
    - "ganimation anatomically aware facial animation from a single image"

Connected through hyperedge HE-2 (induced by source paper "learning continuous face age progression a pyramid of gans"):
  with members:
    - "loss sensitive generative adversarial networks on lipschitz densities"
    - "temporal non volume preserving approach to facial age progression and age invariant face recognition"
    - "learning face age progression a pyramid architecture of gans"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "unsupervised cross domain image generation"
    - "generating images with perceptual similarity metrics based on deep networks"
    - "longitudinal face modeling via temporal deep restricted boltzmann machines"
    - "improved techniques for training gans"

Connected through hyperedge HE-3 (induced by source paper "dual reference age synthesis"):
  with members:
    - "face aging with conditional generative adversarial networks"
    - "exprgan facial expression editing with controllable expression intensity"
    - "learning face age progression a pyramid architecture of gans"
    - "age progression regression by conditional adversarial autoencoder"
    - "stargan unified generative adversarial networks for multi domain image to image translation"
    - "photo realistic facial texture transfer"
    - "fader networks manipulating images by sliding attributes"
    - "conditional generative adversarial nets"

Connected through hyperedge HE-4 (induced by source paper "implicit context aware learning and discovery for streaming data analytics"):
  with members:
    - "attribute recognition by joint recurrent learning of context and correlation"
    - "detecting online hate speech using context aware models"
    - "adaptive convolutional elm for concept drift handling in online stream data"
    - "object detection via aspect ratio and context aware region based convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "gesturegan for hand gesture to gesture translation in the wild"):
  with members:
    - "every smile is unique landmark guided diverse smile generation"
    - "gp gan gender preserving gan for synthesizing faces from landmarks"
    - "geometry contrastive gan for facial expression transfer"
    - "cross domain human parsing via adversarial feature and label adaptation"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "dual discriminator generative adversarial nets"
    - "transformation grounded image generation network for novel 3d view synthesis"
    - "began boundary equilibrium generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "sparsely grouped multi task generative adversarial networks for facial attribute manipulation"):
  with members:
    - "to create what you tell generating videos from captions"
    - "elegant exchanging latent encodings with gan for transferring multiple face attributes"
    - "triangle generative adversarial networks"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "progressive growing of gans for improved quality stability and variation"
    - "good semi supervised learning that requires a bad gan"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unsupervised image to image translation networks"

Connected through hyperedge HE-7 (induced by source paper "global and local consistent wavelet domain age synthesis"):
  with members:
    - "introvae introspective variational autoencoders for photographic image synthesis"
    - "global and local consistent age generative adversarial networks"
    - "temporal non volume preserving approach to facial age progression and age invariant face recognition"
    - "personalized and occupational aware age progression by generative adversarial networks"
    - "unsupervised image to image translation networks"
    - "learning face age progression a pyramid architecture of gans"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "age progression regression by conditional adversarial autoencoder"

Connected through hyperedge HE-8 (induced by source paper "global and local consistent age generative adversarial networks"):
  with members:
    - "learning from longitudinal face demonstration where tractable deep modeling meets inverse reinforcement learning"
    - "temporal non volume preserving approach to facial age progression and age invariant face recognition"
    - "personalized and occupational aware age progression by generative adversarial networks"
    - "learning face age progression a pyramid architecture of gans"
    - "age progression regression by conditional adversarial autoencoder"
    - "learning from simulated and unsupervised images through adversarial training"
    - "longitudinal face modeling via temporal deep restricted boltzmann machines"
    - "face aging effect simulation using hidden factor analysis joint sparse representation"

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.CV(Computer Vision and Pattern Recognition)