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: "context aware cnns for person head detection"
  Abstract: Person detection is a key problem for many computer vision tasks. While face detection has reached maturity, detecting people under a full variation of camera view-points, human poses, lighting conditions and occlusions is still a difficult challenge. In this work we focus on detecting human heads in natural scenes. Starting from the recent local R-CNN object detector, we extend it with two types of contextual cues. First, we leverage person-scene relations and propose a Global CNN model trained to predict positions and scales of heads directly from the full image. Second, we explicitly model pairwise relations among objects and train a Pairwise CNN model using a structured-output surrogate loss. The Local, Global and Pairwise models are combined into a joint CNN framework. To train and test our full model, we introduce a large dataset composed of 369,846 human heads annotated in 224,740 movie frames. We evaluate our method and demonstrate improvements of person head detection against several recent baselines in three datasets. We also show improvements of the detection speed provided by our model.

Connected through hyperedge HE-1 (induced by source paper "relational learning for joint head and human detection"):
  with members:
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "crowdhuman a benchmark for detecting human in a crowd"
    - "focal loss for dense object detection"
    - "citypersons a diverse dataset for pedestrian detection"
    - "what can help pedestrian detection"
    - "cascade r cnn delving into high quality object detection"
    - "repulsion loss detecting pedestrians in a crowd"
    - "to boost or not to boost on the limits of boosted trees for object detection"

Connected through hyperedge HE-2 (induced by source paper "scaling matters in deep structured prediction models"):
  with members:
    - "multimodal machine learning a survey and taxonomy"
    - "searnn training rnns with global local losses"
    - "rethinking atrous convolution for semantic image segmentation"
    - "multi task cross lingual sequence tagging from scratch"
    - "fast exact and multi scale inference for semantic image segmentation with deep gaussian crfs"
    - "u net convolutional networks for biomedical image segmentation"
    - "conditional random fields as recurrent neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-3 (induced by source paper "semantic head enhanced pedestrian detection in a crowd"):
  with members:
    - "high level semantic feature detection a new perspective for pedestrian detection"
    - "mask guided attention network for occluded pedestrian detection"
    - "adaptive nms refining pedestrian detection in a crowd"
    - "cascade r cnn high quality object detection and instance segmentation"
    - "soft nms improving object detection with one line of code"
    - "citypersons a diverse dataset for pedestrian detection"
    - "what can help pedestrian detection"
    - "learning non maximum suppression"

Connected through hyperedge HE-4 (induced by source paper "challenges in time stamp aware anomaly detection in traffic videos"):
  with members:
    - "abnormal event detection in videos using spatiotemporal autoencoder"
    - "learning temporal regularity in video sequences"
    - "fully convolutional neural network for fast anomaly detection in crowded scenes"
    - "flownet learning optical flow with convolutional networks"
    - "learning deep representations of appearance and motion for anomalous event detection"
    - "very deep convolutional networks for large scale image recognition"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "two stream convolutional networks for action recognition in videos"

Connected through hyperedge HE-5 (induced by source paper "deep spatio temporal random fields for efficient video segmentation"):
  with members:
    - "video object segmentation with re identification"
    - "the 2017 davis challenge on video object segmentation"
    - "lucid data dreaming for video object segmentation"
    - "semantic video cnns through representation warping"
    - "online adaptation of convolutional neural networks for video object segmentation"
    - "fusionseg learning to combine motion and appearance for fully automatic segmention of generic objects in videos"
    - "mask r cnn"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-6 (induced by source paper "auto context r cnn"):
  with members:
    - "adversarial patch"
    - "auto context convolutional neural network auto net for brain extraction in magnetic resonance imaging"
    - "non local neural networks"
    - "deformable convolutional networks"
    - "megdet a large mini batch object detector"
    - "adversarial examples for semantic segmentation and object detection"
    - "adversarial examples that fool detectors"
    - "accurate single stage detector using recurrent rolling convolution"

Connected through hyperedge HE-7 (induced by source paper "detecting heads using feature refine net and cascaded multi scale architecture"):
  with members:
    - "face detection through scale friendly deep convolutional networks"
    - "perceptual generative adversarial networks for small object detection"
    - "ssd single shot multibox detector"
    - "training region based object detectors with online hard example mining"
    - "yolo9000 better faster stronger"
    - "hypernet towards accurate region proposal generation and joint object detection"
    - "feature pyramid networks for object detection"
    - "finding tiny faces"

Connected through hyperedge HE-8 (induced by source paper "tube cnn modeling temporal evolution of appearance for object detection in video"):
  with members:
    - "object detection in videos with tubelet proposal networks"
    - "learning by tracking siamese cnn for robust target association"
    - "object detection from video tubelets with convolutional neural networks"
    - "training region based object detectors with online hard example mining"
    - "learning to segment object candidates"
    - "locnet improving localization accuracy for object detection"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "deep residual learning for image recognition"

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)