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: "deep alignment network a convolutional neural network for robust face alignment"
  Abstract: In this paper, we propose Deep Alignment Network (DAN), a robust face alignment method based on a deep neural network architecture. DAN consists of multiple stages, where each stage improves the locations of the facial landmarks estimated by the previous stage. Our method uses entire face images at all stages, contrary to the recently proposed face alignment methods that rely on local patches. This is possible thanks to the use of landmark heatmaps which provide visual information about landmark locations estimated at the previous stages of the algorithm. The use of entire face images rather than patches allows DAN to handle face images with large variation in head pose and difficult initializations. An extensive evaluation on two publicly available datasets shows that DAN reduces the state-of-the-art failure rate by up to 70%. Our method has also been submitted for evaluation as part of the Menpo challenge.

Connected through hyperedge HE-1 (induced by source paper "joint face alignment and 3d face reconstruction with application to face recognition"):
  with members:
    - "representation learning by rotating your faces"
    - "densereg fully convolutional dense shape regression in the wild"
    - "nonlinear 3d face morphable model"
    - "pose invariant face alignment with a single cnn"
    - "a recurrent encoder decoder network for sequential face alignment"
    - "robust face alignment using a mixture of invariant experts"
    - "fitting a 3d morphable model to edges a comparison between hard and soft correspondences"
    - "regressing robust and discriminative 3d morphable models with a very deep neural network"

Connected through hyperedge HE-2 (induced by source paper "multistage model for robust face alignment using deep neural networks"):
  with members:
    - "joint face alignment and 3d face reconstruction with application to face recognition"
    - "face alignment using a 3d deeply initialized ensemble of regression trees"
    - "look at boundary a boundary aware face alignment algorithm"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "style aggregated network for facial landmark detection"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "joint multi view face alignment in the wild"

Connected through hyperedge HE-3 (induced by source paper "adaptive wing loss for robust face alignment via heatmap regression"):
  with members:
    - "quantized densely connected u nets for efficient landmark localization"
    - "look at boundary a boundary aware face alignment algorithm"
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "densereg fully convolutional dense shape regression in the wild"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "arcface additive angular margin loss for deep face recognition"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"

Connected through hyperedge HE-4 (induced by source paper "mobilefan transferring deep hidden representation for face alignment"):
  with members:
    - "structured knowledge distillation for dense prediction"
    - "look at boundary a boundary aware face alignment algorithm"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "multi label learning based deep transfer neural network for facial attribute classification"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "simple baselines for human pose estimation and tracking"
    - "improving fast segmentation with teacher student learning"

Connected through hyperedge HE-5 (induced by source paper "anonymousnet natural face de identification with measurable privacy"):
  with members:
    - "beauty learning and counterfactual inference"
    - "understanding beauty via deep facial features"
    - "semi supervised text regression with conditional generative adversarial networks"
    - "privacy protective gan for face de identification"
    - "technical privacy metrics a systematic survey"
    - "privacy preserving machine learning through data obfuscation"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "style aggregated network for facial landmark detection"

Connected through hyperedge HE-6 (induced by source paper "attentional feature pair relation networks for accurate face recognition"):
  with members:
    - "arcface additive angular margin loss for deep face recognition"
    - "a fast and accurate system for face detection identification and verification"
    - "pairwise relational networks for face recognition"
    - "comparator networks"
    - "pose robust face recognition via deep residual equivariant mapping"
    - "beyond bilinear generalized multi modal factorized high order pooling for visual question answering"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "cross age lfw a database for studying cross age face recognition in unconstrained environments"

Connected through hyperedge HE-7 (induced by source paper "pose guided structured region ensemble network for cascaded hand pose estimation"):
  with members:
    - "intel realsense stereoscopic depth cameras"
    - "region ensemble network improving convolutional network for hand pose estimation"
    - "end to end global to local cnn learning for hand pose recovery in depth data"
    - "dynamics based 3d skeletal hand tracking"
    - "motion feature augmented recurrent neural network for skeleton based dynamic hand gesture recognition"
    - "mask r cnn"
    - "training a feedback loop for hand pose estimation"
    - "robust 3d hand pose estimation in single depth images from single view cnn to multi view cnns"

Connected through hyperedge HE-8 (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"

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)