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: "occlusion coherence detecting and localizing occluded faces"
  Abstract: The presence of occluders significantly impacts object recognition accuracy. However, occlusion is typically treated as an unstructured source of noise and explicit models for occluders have lagged behind those for object appearance and shape. In this paper we describe a hierarchical deformable part model for face detection and landmark localization that explicitly models part occlusion. The proposed model structure makes it possible to augment positive training data with large numbers of synthetically occluded instances. This allows us to easily incorporate the statistics of occlusion patterns in a discriminatively trained model. We test the model on several benchmarks for landmark localization and detection including challenging new data sets featuring significant occlusion. We find that the addition of an explicit occlusion model yields a detection system that outperforms existing approaches for occluded instances while maintaining competitive accuracy in detection and landmark localization for unoccluded instances.

Connected through hyperedge HE-1 (induced by source paper "robust facial landmark detection via aggregation on geometrically manipulated faces"):
  with members:
    - "spatially transformed adversarial examples"
    - "fast geometrically perturbed adversarial faces"
    - "style aggregated network for facial landmark detection"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "pose invariant face alignment with a single cnn"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "simultaneous facial landmark detection pose and deformation estimation under facial occlusion"
    - "more is less a more complicated network with less inference complexity"

Connected through hyperedge HE-2 (induced by source paper "refineface refinement neural network for high performance face detection"):
  with members:
    - "robust and high performance face detector"
    - "high level semantic feature detection a new perspective for pedestrian detection"
    - "pyramidbox high performance detector for finding tiny face"
    - "retinaface single stage dense face localisation in the wild"
    - "accurate face detection for high performance"
    - "improved selective refinement network for face detection"
    - "dsfd dual shot face detector"
    - "face detection using improved faster rcnn"

Connected through hyperedge HE-3 (induced by source paper "revisiting a single stage method for face detection"):
  with members:
    - "seeing small faces from robust anchor s perspective"
    - "cms rcnn contextual multi scale region based cnn for unconstrained face detection"
    - "focal loss for dense object detection"
    - "face attention network an effective face detector for the occluded faces"
    - "face detection through scale friendly deep convolutional networks"
    - "face detection using deep learning an improved faster rcnn approach"
    - "ssh single stage headless face detector"
    - "to boost or not to boost on the limits of boosted trees for object detection"

Connected through hyperedge HE-4 (induced by source paper "animalweb a large scale hierarchical dataset of annotated animal faces"):
  with members:
    - "quantized densely connected u nets for efficient landmark localization"
    - "multi attention multi class constraint for fine grained image recognition"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "interspecies knowledge transfer for facial keypoint detection"
    - "the inaturalist species classification and detection dataset"
    - "human and sheep facial landmarks localisation by triplet interpolated features"
    - "deep residual learning for image recognition"
    - "wider face a face detection benchmark"

Connected through hyperedge HE-5 (induced by source paper "pyramidbox a context assisted single shot face detector"):
  with members:
    - "cms rcnn contextual multi scale region based cnn for unconstrained face detection"
    - "s 3 fd single shot scale invariant face detector"
    - "focal loss for dense object detection"
    - "single shot refinement neural network for object detection"
    - "ssh single stage headless face detector"
    - "face r cnn"
    - "to boost or not to boost on the limits of boosted trees for object detection"
    - "faceboxes a cpu real time face detector with high accuracy"

Connected through hyperedge HE-6 (induced by source paper "seeing small faces from robust anchor s perspective"):
  with members:
    - "faster than real time facial alignment a 3d spatial transformer network approach in unconstrained poses"
    - "cms rcnn contextual multi scale region based cnn for unconstrained face detection"
    - "sphereface deep hypersphere embedding for face recognition"
    - "deformable convolutional networks"
    - "face detection through scale friendly deep convolutional networks"
    - "face detection using deep learning an improved faster rcnn approach"
    - "ssh single stage headless face detector"
    - "to boost or not to boost on the limits of boosted trees for object detection"

Connected through hyperedge HE-7 (induced by source paper "stacked dense u nets with dual transformers for robust face alignment"):
  with members:
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "arcface additive angular margin loss for deep face recognition"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "improving landmark localization with semi supervised learning"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deformable convolutional networks"
    - "squeeze and excitation networks"

Connected through hyperedge HE-8 (induced by source paper "facial landmark point localization using coarse to fine deep recurrent neural network"):
  with members:
    - "deep alignment network a convolutional neural network for robust face alignment"
    - "improving landmark localization with semi supervised learning"
    - "adversarial learning of structure aware fully convolutional networks for landmark localization"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "recurrent human pose estimation"
    - "robust face alignment using a mixture of invariant experts"
    - "learning deep representation for face alignment with auxiliary attributes"
    - "two stage convolutional part heatmap regression for the 1st 3d face alignment in the wild 3dfaw challenge"

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)