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: "reconstruction for feature disentanglement in pose invariant face recognition"
  Abstract: Deep neural networks (DNNs) trained on large-scale datasets have recently achieved impressive improvements in face recognition. But a persistent challenge remains to develop methods capable of handling large pose variations that are relatively under-represented in training data. This paper presents a method for learning a feature representation that is invariant to pose, without requiring extensive pose coverage in training data. We first propose to use a synthesis network for generating non-frontal views from a single frontal image, in order to increase the diversity of training data while preserving accurate facial details that are critical for identity discrimination. Our next contribution is a multi-source multi-task DNN that seeks a rich embedding representing identity information, as well as information such as pose and landmark locations. Finally, we propose a Siamese network to explicitly disentangle identity and pose, by demanding alignment between the feature reconstructions through various combinations of identity and pose features obtained from two images of the same subject. Experiments on face datasets in both controlled and wild scenarios, such as MultiPIE, LFW and 300WLP, show that our method consistently outperforms the state-of-the-art, especially on images with large head pose variations.

Connected through hyperedge HE-1 (induced by source paper "lcanet end to end lipreading with cascaded attention ctc"):
  with members:
    - "progressive joint modeling in unsupervised single channel overlapped speech recognition"
    - "draps dynamic and resource aware placement scheme for docker containers in a heterogeneous cluster"
    - "deeprebirth accelerating deep neural network execution on mobile devices"
    - "attention is all you need"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "end to end visual speech recognition with lstms"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-2 (induced by source paper "density aware single image de raining using a multi stream dense network"):
  with members:
    - "residual dense network for image super resolution"
    - "context encoding for semantic segmentation"
    - "densely connected pyramid dehazing network"
    - "generating high quality crowd density maps using contextual pyramid cnns"
    - "multi style generative network for real time transfer"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "clearing the skies a deep network architecture for single image rain removal"
    - "mdnet a semantically and visually interpretable medical image diagnosis network"

Connected through hyperedge HE-3 (induced by source paper "learning not to learn training deep neural networks with biased data"):
  with members:
    - "turning a blind eye explicit removal of biases and variation from deep neural network embeddings"
    - "women also snowboard overcoming bias in captioning models"
    - "pairwise confusion for fine grained visual classification"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "simultaneous deep transfer across domains and tasks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-4 (induced by source paper "recent progress of face image synthesis"):
  with members:
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "generative face completion"
    - "random sampling and locality constraint for face sketch"
    - "face aging with conditional generative adversarial networks"
    - "srefi synthesis of realistic example face images"
    - "genegan learning object transfiguration and attribute subspace from unpaired data"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "multi view image generation from a single view"

Connected through hyperedge HE-5 (induced by source paper "disentangling factors of variation by mixing them"):
  with members:
    - "began boundary equilibrium generative adversarial networks"
    - "variational inference of disentangled latent concepts from unlabeled observations"
    - "deep mean shift priors for image restoration"
    - "progressive growing of gans for improved quality stability and variation"
    - "unsupervised learning of disentangled representations from video"
    - "unsupervised learning of depth and ego motion from video"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "neural face editing with intrinsic image disentangling"

Connected through hyperedge HE-6 (induced by source paper "challenges in disentangling independent factors of variation"):
  with members:
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"
    - "adversarial feature learning"
    - "weakly supervised disentangling with recurrent transformations for 3d view synthesis"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "shapenet an information rich 3d model repository"
    - "very deep convolutional networks for large scale image recognition"
    - "discovering hidden factors of variation in deep networks"

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)