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: "a fast and accurate unconstrained face detector"
  Abstract: We propose a method to address challenges in unconstrained face detection, such as arbitrary pose variations and occlusions. First, a new image feature called Normalized Pixel Difference (NPD) is proposed. NPD feature is computed as the difference to sum ratio between two pixel values, inspired by the Weber Fraction in experimental psychology. The new feature is scale invariant, bounded, and is able to reconstruct the original image. Second, we propose a deep quadratic tree to learn the optimal subset of NPD features and their combinations, so that complex face manifolds can be partitioned by the learned rules. This way, only a single soft-cascade classifier is needed to handle unconstrained face detection. Furthermore, we show that the NPD features can be efficiently obtained from a look up table, and the detection template can be easily scaled, making the proposed face detector very fast. Experimental results on three public face datasets (FDDB, GENKI, and CMU-MIT) show that the proposed method achieves state-of-the-art performance in detecting unconstrained faces with arbitrary pose variations and occlusions in cluttered scenes.

Connected through hyperedge HE-1 (induced by source paper "a robust real time computing based environment sensing system for intelligent vehicle"):
  with members:
    - "pseudo lidar from visual depth estimation bridging the gap in 3d object detection for autonomous driving"

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 "accurate face detection for high performance"):
  with members:
    - "robust and high performance face detector"
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "pyramidbox high performance detector for finding tiny face"
    - "improved selective refinement network for face detection"
    - "dsfd dual shot face detector"
    - "face detection using improved faster rcnn"
    - "pyramidbox a context assisted single shot face detector"
    - "selective refinement network for high performance face detection"

Connected through hyperedge HE-4 (induced by source paper "robust and high performance face detector"):
  with members:
    - "pyramidbox a context assisted single shot face detector"
    - "selective refinement network for high performance face detection"
    - "beyond trade off accelerate fcn based face detector with higher accuracy"
    - "learning transferable architectures for scalable image recognition"
    - "focal loss for dense object detection"
    - "face attention network an effective face detector for the occluded faces"
    - "cascade r cnn delving into high quality object detection"
    - "single shot refinement neural network for object detection"

Connected through hyperedge HE-5 (induced by source paper "msfd multi scale receptive field face detector"):
  with members:
    - "s 3 fd single shot scale invariant face detector"
    - "focal loss for dense object detection"
    - "detecting faces using region based fully 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"
    - "faceboxes a cpu real time face detector with high accuracy"
    - "face detection with the faster r cnn"

Connected through hyperedge HE-6 (induced by source paper "sfa small faces attention face detector"):
  with members:
    - "cms rcnn contextual multi scale region based cnn for unconstrained face detection"
    - "pose invariant face alignment with a single cnn"
    - "detecting faces using region based fully convolutional networks"
    - "face detection using deep learning an improved faster rcnn approach"
    - "recurrent scale approximation for object detection in cnn"
    - "ssh single stage headless face detector"
    - "face r cnn"
    - "to boost or not to boost on the limits of boosted trees for object detection"

Connected through hyperedge HE-7 (induced by source paper "real world attack on mtcnn face detection system"):
  with members:
    - "daedalus breaking non maximum suppression in object detection via adversarial examples"
    - "retinaface single stage dense face localisation in the wild"
    - "fooling automated surveillance cameras adversarial patches to attack person detection"
    - "arcface additive angular margin loss for deep face recognition"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "synthesizing robust adversarial examples"
    - "practical black box attacks against machine learning"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-8 (induced by source paper "facial age estimation by deep residual decision making"):
  with members:
    - "hierarchical binary cnns for landmark localization with limited resources"
    - "visualizing the decision making process in deep neural decision forest"
    - "interpreting cnns via decision trees"
    - "interpretable learning for self driving cars by visualizing causal attention"
    - "visualizing the loss landscape of neural nets"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "label distribution learning forests"
    - "interpretable 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)