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: "learning a high fidelity pose invariant model for high resolution face frontalization"
  Abstract: Face frontalization refers to the process of synthesizing the frontal view of a face from a given profile. Due to self-occlusion and appearance distortion in the wild, it is extremely challenging to recover faithful results and preserve texture details in a high-resolution. This paper proposes a High Fidelity Pose Invariant Model (HF-PIM) to produce photographic and identity-preserving results. HF-PIM frontalizes the profiles through a novel texture warping procedure and leverages a dense correspondence field to bind the 2D and 3D surface spaces. We decompose the prerequisite of warping into dense correspondence field estimation and facial texture map recovering, which are both well addressed by deep networks. Different from those reconstruction methods relying on 3D data, we also propose Adversarial Residual Dictionary Learning (ARDL) to supervise facial texture map recovering with only monocular images. Exhaustive experiments on both controlled and uncontrolled environments demonstrate that the proposed method not only boosts the performance of pose-invariant face recognition but also dramatically improves high-resolution frontalization appearances.

Connected through hyperedge HE-1 (induced by source paper "rotate and render unsupervised photorealistic face rotation from single view images"):
  with members:
    - "maskgan towards diverse and interactive facial image manipulation"
    - "representation learning by rotating your faces"
    - "learning correspondence from the cycle consistency of time"
    - "semantic image synthesis with spatially adaptive normalization"
    - "face alignment in full pose range a 3d total solution"
    - "make a face towards arbitrary high fidelity face manipulation"
    - "learning to predict layout to image conditional convolutions for semantic image synthesis"
    - "vision infused deep audio inpainting"

Connected through hyperedge HE-2 (induced by source paper "dual attention gan for large pose face frontalization"):
  with members:
    - "joint super resolution and alignment of tiny faces"
    - "cr gan learning complete representations for multi view generation"
    - "unsupervised attention guided image to image translation"
    - "cbam convolutional block attention module"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "attention is all you need"
    - "rethinking spatiotemporal feature learning speed accuracy trade offs in video classification"

Connected through hyperedge HE-3 (induced by source paper "textured neural avatars"):
  with members:
    - "neural rendering and reenactment of human actor videos"
    - "video to video synthesis"
    - "deep video portraits"
    - "synthesizing images of humans in unseen poses"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "densereg fully convolutional dense shape regression in the wild"
    - "deep video based performance cloning"
    - "lookingood enhancing performance capture with real time neural re rendering"

Connected through hyperedge HE-4 (induced by source paper "biphasic learning of gans for high resolution image to image translation"):
  with members:
    - "cgans with projection discriminator"
    - "instance level facial attributes transfer with geometry aware flow"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "ganimation anatomically aware facial animation from a single image"
    - "mine mutual information neural estimation"
    - "representation learning with contrastive predictive coding"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-5 (induced by source paper "m2fpa a multi yaw multi pitch high quality database and benchmark for facial pose analysis"):
  with members:
    - "cr gan learning complete representations for multi view generation"
    - "unsupervised depth estimation 3d face rotation and replacement"
    - "a style based generator architecture for generative adversarial networks"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "progressive growing of gans for improved quality stability and variation"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "towards large pose face frontalization in the wild"

Connected through hyperedge HE-6 (induced by source paper "lglg wpca an effective texture based method for face recognition"):
  with members:
    - "multi fold gabor pca and ica filter convolution descriptor for face recognition"
    - "covariance pooling for facial expression recognition"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "squeeze and excitation networks"
    - "towards faster training of global covariance pooling networks by iterative matrix square root normalization"
    - "dimensionality reduction on spd manifolds the emergence of geometry aware methods"
    - "kernel methods on approximate infinite dimensional covariance operators for image 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.CV(Computer Vision and Pattern Recognition)