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: "skeleton aided articulated motion generation"
  Abstract: This work make the first attempt to generate articulated human motion sequence from a single image. On the one hand, we utilize paired inputs including human skeleton information as motion embedding and a single human image as appearance reference, to generate novel motion frames, based on the conditional GAN infrastructure. On the other hand, a triplet loss is employed to pursue appearance-smoothness between consecutive frames. As the proposed framework is capable of jointly exploiting the image appearance space and articulated/kinematic motion space, it generates realistic articulated motion sequence, in contrast to most previous video generation methods which yield blurred motion effects. We test our model on two human action datasets including KTH and Human3.6M, and the proposed framework generates very promising results on both datasets.

Connected through hyperedge HE-1 (induced by source paper "multi channel attention selection gans for guided image to image translation"):
  with members:
    - "progressive pose attention transfer for person image generation"
    - "local class specific and global image level generative adversarial networks for semantic guided scene generation"
    - "example guided style consistent image synthesis from semantic labeling"
    - "asymmetric generative adversarial networks for image to image translation"
    - "u gat it unsupervised generative attentional networks with adaptive layer instance normalization for image to image translation"
    - "multi channel attention selection gan with cascaded semantic guidance for cross view image translation"
    - "cycle in cycle generative adversarial networks for keypoint guided image generation"
    - "attentiongan unpaired image to image translation using attention guided generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "a robust pose transformational gan for pose guided person image synthesis"):
  with members:
    - "soft gated warping gan for pose guided person image synthesis"
    - "synthesizing images of humans in unseen poses"
    - "stellar cluster detection using gmm with deep variational autoencoder"
    - "fd gan pose guided feature distilling gan for robust person re identification"
    - "random erasing data augmentation"
    - "pose guided person image generation"
    - "pose normalized image generation for person re identification"
    - "disentangled person image generation"

Connected through hyperedge HE-3 (induced by source paper "cycle in cycle generative adversarial networks for keypoint guided image generation"):
  with members:
    - "multi channel attention selection gan with cascaded semantic guidance for cross view image translation"
    - "gazecorrection self guided eye manipulation in the wild using self supervised generative adversarial networks"
    - "semantic image synthesis with spatially adaptive normalization"
    - "cascade attention guided residue learning gan for cross modal translation"
    - "attention guided generative adversarial networks for unsupervised image to image translation"
    - "dual generator generative adversarial networks for multi domain image to image translation"
    - "video to video synthesis"
    - "cross view image synthesis using conditional gans"

Connected through hyperedge HE-4 (induced by source paper "generating human action videos by coupling 3d game engines and probabilistic graphical models"):
  with members:
    - "pose guided human video generation"
    - "mt vae learning motion transformations to generate multimodal human dynamics"
    - "reenactgan learning to reenact faces via boundary transfer"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "attentive semantic video generation using captions"
    - "carla an open urban driving simulator"
    - "temporal segment networks for action recognition in videos"
    - "video generation from text"

Connected through hyperedge HE-5 (induced by source paper "unified generative adversarial networks for controllable image to image translation"):
  with members:
    - "attribute guided sketch generation"
    - "multi channel attention selection gan with cascaded semantic guidance for cross view image translation"
    - "cycle in cycle generative adversarial networks for keypoint guided image generation"
    - "semantic image synthesis with spatially adaptive normalization"
    - "dual generator generative adversarial networks for multi domain image to image translation"
    - "cross view image synthesis using conditional gans"
    - "gesturegan for hand gesture to gesture translation in the wild"
    - "sketchygan towards diverse and realistic sketch to image synthesis"

Connected through hyperedge HE-6 (induced by source paper "gesture to gesture translation in the wild via category independent conditional maps"):
  with members:
    - "3d guided fine grained face manipulation"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "gesturegan for hand gesture to gesture translation in the wild"
    - "pros and cons of gan evaluation measures"
    - "triple consistency loss for pairing distributions in gan based face synthesis"
    - "instagan instance aware image to image translation"
    - "ganimation anatomically aware facial animation from a single image"
    - "animating arbitrary objects via deep motion transfer"

Connected through hyperedge HE-7 (induced by source paper "asymmetric generative adversarial networks for image to image translation"):
  with members:
    - "expression conditional gan for facial expression to expression translation"
    - "auto embedding generative adversarial networks for high resolution image synthesis"
    - "multi channel attention selection gan with cascaded semantic guidance for cross view image translation"
    - "cycle in cycle generative adversarial networks for keypoint guided image generation"
    - "cascade attention guided residue learning gan for cross modal translation"
    - "attention guided generative adversarial networks for unsupervised image to image translation"
    - "gesturegan for hand gesture to gesture translation in the wild"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-8 (induced by source paper "towards multi pose guided virtual try on network"):
  with members:
    - "synthesizing images of humans in unseen poses"
    - "unsupervised person image synthesis in arbitrary poses"
    - "toward characteristic preserving image based virtual try on network"
    - "a variational u net for conditional appearance and shape generation"
    - "deepwrinkles accurate and realistic clothing modeling"
    - "viton an image based virtual try on network"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "detailed accurate human shape estimation from clothed 3d scan sequences"

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)