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: "transferring landmark annotations for cross dataset face alignment"
  Abstract: Dataset bias is a well known problem in object recognition domain. This issue, nonetheless, is rarely explored in face alignment research. In this study, we show that dataset plays an integral part of face alignment performance. Specifically, owing to face alignment dataset bias, training on one database and testing on another or unseen domain would lead to poor performance. Creating an unbiased dataset through combining various existing databases, however, is non-trivial as one has to exhaustively re-label the landmarks for standardisation. In this work, we propose a simple and yet effective method to bridge the disparate annotation spaces between databases, making datasets fusion possible. We show extensive results on combining various popular databases (LFW, AFLW, LFPW, HELEN) for improved cross-dataset and unseen data alignment.

Connected through hyperedge HE-1 (induced by source paper "facial landmark correlation analysis"):
  with members:
    - "similarity of neural network representations revisited"
    - "teacher supervises students how to learn from partially labeled images for facial landmark detection"
    - "facial landmark detection a literature survey"
    - "decafa deep convolutional cascade for face alignment in the wild"
    - "look at boundary a boundary aware face alignment algorithm"
    - "towards highly accurate and stable face alignment for high resolution videos"
    - "deep multi center learning for face alignment"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "2d wasserstein loss for robust facial landmark detection"):
  with members:
    - "semantic alignment finding semantically consistent ground truth for facial landmark detection"
    - "high resolution representations for labeling pixels and regions"
    - "adaptive wing loss for robust face alignment via heatmap regression"
    - "look at boundary a boundary aware face alignment algorithm"
    - "towards highly accurate and stable face alignment for high resolution videos"
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "simple baselines for human pose estimation and tracking"

Connected through hyperedge HE-3 (induced by source paper "look at boundary a boundary aware face alignment algorithm"):
  with members:
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "densereg fully convolutional dense shape regression in the wild"
    - "style aggregated network for facial landmark detection"
    - "learning feature pyramids for human pose estimation"
    - "self adversarial training for human pose estimation"
    - "casenet deep category aware semantic edge detection"
    - "adversarial posenet a structure aware convolutional network for human pose estimation"
    - "simultaneous facial landmark detection pose and deformation estimation under facial occlusion"

Connected through hyperedge HE-4 (induced by source paper "faceness net face detection through deep facial part responses"):
  with members:
    - "face detection through scale friendly deep convolutional networks"
    - "face detection with the faster r cnn"
    - "grid loss detecting occluded faces"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "finding tiny faces"
    - "supervised transformer network for efficient face detection"
    - "multi view face detection using deep convolutional neural networks"
    - "wider face a face detection benchmark"

Connected through hyperedge HE-5 (induced by source paper "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"):
  with members:
    - "binarized convolutional landmark localizers for human pose estimation and face alignment with limited resources"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "identity mappings in deep residual networks"
    - "deepercut a deeper stronger and faster multi person pose estimation model"
    - "two stage convolutional part heatmap regression for the 1st 3d face alignment in the wild 3dfaw challenge"
    - "human pose estimation via convolutional part heatmap regression"
    - "cascaded continuous regression for real time incremental face tracking"

Connected through hyperedge HE-6 (induced by source paper "dense face alignment"):
  with members:
    - "3d face morphable models in the wild"
    - "learning deep representation for face alignment with auxiliary attributes"
    - "multi directional multi level dual cross patterns for robust face recognition"
    - "regressing robust and discriminative 3d morphable models with a very deep neural network"
    - "holistically nested edge detection"
    - "pose invariant 3d face alignment"
    - "face alignment assisted by head pose estimation"
    - "mirror mirror on the wall tell me is the error small"

Connected through hyperedge HE-7 (induced by source paper "convolutional network for attribute driven and identity preserving human face generation"):
  with members:
    - "perceptual losses for real time style transfer and super resolution"
    - "a neural algorithm of artistic style"
    - "attribute2image conditional image generation from visual attributes"
    - "vqa visual question answering"
    - "deep convolutional inverse graphics network"
    - "what makes kevin spacey look like kevin spacey"
    - "learning a discriminative model for the perception of realism in composite images"
    - "draw a recurrent neural network for image generation"

Connected through hyperedge HE-8 (induced by source paper "attribute2image conditional image generation from visual attributes"):
  with members:
    - "understanding neural networks through deep visualization"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep convolutional inverse graphics network"
    - "text to 3d scene generation with rich lexical grounding"
    - "deep generative image models using a laplacian pyramid of adversarial networks"
    - "texture synthesis using convolutional neural networks"
    - "draw a recurrent neural network for image generation"
    - "adam a method for stochastic optimization"

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)