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 alignment in the wild a survey"
  Abstract: Over the last two decades, face alignment or localizing fiducial facial points has received increasing attention owing to its comprehensive applications in automatic face analysis. However, such a task has proven extremely challenging in unconstrained environments due to many confounding factors, such as pose, occlusions, expression and illumination. While numerous techniques have been developed to address these challenges, this problem is still far away from being solved. In this survey, we present an up-to-date critical review of the existing literatures on face alignment, focusing on those methods addressing overall difficulties and challenges of this topic under uncontrolled conditions. Specifically, we categorize existing face alignment techniques, present detailed descriptions of the prominent algorithms within each category, and discuss their advantages and disadvantages. Furthermore, we organize special discussions on the practical aspects of face alignment in-the-wild, towards the development of a robust face alignment system. In addition, we show performance statistics of the state of the art, and conclude this paper with several promising directions for future research.

Connected through hyperedge HE-1 (induced by source paper "end to end face parsing via interlinked convolutional neural networks"):
  with members:
    - "face parsing with roi tanh warping"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "face parsing via a fully convolutional continuous crf neural network"
    - "adversarial deep structured nets for mass segmentation from mammograms"
    - "mask r cnn"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "a cnn cascade for landmark guided semantic part segmentation"

Connected through hyperedge HE-2 (induced by source paper "lffd a light and fast face detector for edge devices"):
  with members:
    - "robust and high performance face detector"
    - "improved selective refinement network for face detection"
    - "dsfd dual shot face detector"
    - "seeing small faces from robust anchor s perspective"
    - "yolov3 an incremental improvement"
    - "deep face recognition a survey"
    - "pyramidbox a context assisted single shot face detector"
    - "selective refinement network for high performance face detection"

Connected through hyperedge HE-3 (induced by source paper "a fast online cascaded regression algorithm for face alignment"):
  with members:
    - "3 d personvlad learning deep global representations for video based person reidentification"
    - "a robust approach for securing audio classification against adversarial attacks"
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "cross entropy adversarial view adaptation for person re identification"
    - "face alignment in full pose range a 3d total solution"
    - "robust tracking via weighted online extreme learning machine"
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "a functional regression approach to facial landmark tracking"

Connected through hyperedge HE-4 (induced by source paper "face alignment using a 3d deeply initialized ensemble of regression trees"):
  with members:
    - "face alignment in full pose range a 3d total solution"
    - "look at boundary a boundary aware face alignment algorithm"
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "deep alignment network a convolutional neural network for robust face alignment"
    - "pose invariant face alignment with a single cnn"
    - "binarized convolutional landmark localizers for human pose estimation and face alignment with limited resources"
    - "robust facial landmark detection under significant head poses and occlusion"
    - "dynamic attention controlled cascaded shape regression exploiting training data augmentation and fuzzy set sample weighting"

Connected through hyperedge HE-5 (induced by source paper "deep energies for estimating three dimensional facial pose and expression"):
  with members:
    - "look at boundary a boundary aware face alignment algorithm"
    - "supervision by registration an unsupervised approach to improve the precision of facial landmark detectors"
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "generative face completion"
    - "deep image prior"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"

Connected through hyperedge HE-6 (induced by source paper "self supervised cnn for unconstrained 3d facial performance capture from an rgb d camera"):
  with members:
    - "3d face hallucination from a single depth frame"
    - "faster than real time facial alignment a 3d spatial transformer network approach in unconstrained poses"
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "learning dense facial correspondences in unconstrained images"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "unrestricted facial geometry reconstruction using image to image translation"
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"
    - "self supervised multi level face model learning for monocular reconstruction at over 250 hz"

Connected through hyperedge HE-7 (induced by source paper "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"):
  with members:
    - "densereg fully convolutional dense shape regression in the wild"
    - "real time 3d face fitting and texture fusion on in the wild videos"
    - "facial performance capture with deep neural networks"
    - "a recurrent encoder decoder network for sequential face alignment"
    - "production level facial performance capture using deep convolutional neural networks"
    - "deep cascaded bi network for face hallucination"
    - "robust lstm autoencoders for face de occlusion in the wild"
    - "perspective transformer nets learning single view 3d object reconstruction without 3d supervision"

Connected through hyperedge HE-8 (induced by source paper "inversefacenet deep single shot inverse face rendering from a single image"):
  with members:
    - "a comprehensive performance evaluation of deformable face tracking in the wild"
    - "3d face reconstruction with geometry details from a single image"
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "3d face morphable models in the wild"
    - "facial performance capture with deep neural networks"
    - "a recurrent encoder decoder network for sequential face alignment"
    - "failure detection for facial landmark detectors"
    - "production level facial performance capture using deep convolutional neural 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)