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: "attentionnet aggregating weak directions for accurate object detection"
  Abstract: We present a novel detection method using a deep convolutional neural network (CNN), named AttentionNet. We cast an object detection problem as an iterative classification problem, which is the most suitable form of a CNN. AttentionNet provides quantized weak directions pointing a target object and the ensemble of iterative predictions from AttentionNet converges to an accurate object boundary box. Since AttentionNet is a unified network for object detection, it detects objects without any separated models from the object proposal to the post bounding-box regression. We evaluate AttentionNet by a human detection task and achieve the state-of-the-art performance of 65% (AP) on PASCAL VOC 2007/2012 with an 8-layered architecture only.

Connected through hyperedge HE-1 (induced by source paper "cascade r cnn high quality object detection and instance segmentation"):
  with members:
    - "hybrid task cascade for instance segmentation"
    - "rethinking imagenet pre training"
    - "cornernet detecting objects as paired keypoints"
    - "group normalization"
    - "single shot bidirectional pyramid networks for high quality object detection"
    - "acquisition of localization confidence for accurate object detection"
    - "path aggregation network for instance segmentation"
    - "soft nms improving object detection with one line of code"

Connected through hyperedge HE-2 (induced by source paper "on the detection of digital face manipulation"):
  with members:
    - "attention based dropout layer for weakly supervised object localization"
    - "multi task learning for detecting and segmenting manipulated facial images and videos"
    - "faceforensics learning to detect manipulated facial images"
    - "advfaces adversarial face synthesis"
    - "deep tree learning for zero shot face anti spoofing"
    - "representation learning by rotating your faces"
    - "on learning 3d face morphable model from in the wild images"
    - "fakespotter a simple baseline for spotting ai synthesized fake faces"

Connected through hyperedge HE-3 (induced by source paper "multi scale attention network for crowd counting"):
  with members:
    - "divide and grow capturing huge diversity in crowd images with incrementally growing cnn"
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "detecting heads using feature refine net and cascaded multi scale architecture"
    - "crowd counting by adaptively fusing predictions from an image pyramid"
    - "pyramid attention network for semantic segmentation"
    - "switching convolutional neural network for crowd counting"
    - "decidenet counting varying density crowds through attention guided detection and density estimation"

Connected through hyperedge HE-4 (induced by source paper "assd attentive single shot multibox detector"):
  with members:
    - "cornernet detecting objects as paired keypoints"
    - "learn to pay attention"
    - "fssd feature fusion single shot multibox detector"
    - "focal loss for dense object detection"
    - "attention is all you need"
    - "relation networks for object detection"
    - "dssd deconvolutional single shot detector"
    - "single shot refinement neural network for object detection"

Connected through hyperedge HE-5 (induced by source paper "3d objectness estimation via bottom up regret grouping"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "object detection with deep learning a review"
    - "orthographic feature transform for monocular 3d object detection"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "sgpn similarity group proposal network for 3d point cloud instance segmentation"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "squeezeseg convolutional neural nets with recurrent crf for real time road object segmentation from 3d lidar point cloud"

Connected through hyperedge HE-6 (induced by source paper "fpan fine grained and progressive attention localization network for data retrieval"):
  with members:
    - "fine grained discriminative localization via saliency guided faster r cnn"
    - "dssd deconvolutional single shot detector"
    - "dual deep network for visual tracking"
    - "ensemble of part detectors for simultaneous classification and localization"
    - "stnet selective tuning of convolutional networks for object localization"
    - "progressive attention networks for visual attribute prediction"
    - "beyond correlation filters learning continuous convolution operators for visual tracking"
    - "xception deep learning with depthwise separable convolutions"

Connected through hyperedge HE-7 (induced by source paper "spatial memory for context reasoning in object detection"):
  with members:
    - "an implementation of faster rcnn with study for region sampling"
    - "neural map structured memory for deep reinforcement learning"
    - "deep variation structured reinforcement learning for visual relationship and attribute detection"
    - "cognitive mapping and planning for visual navigation"
    - "end to end instance segmentation and counting with recurrent attention"
    - "ssd single shot multibox detector"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-8 (induced by source paper "single shot refinement neural network for object detection"):
  with members:
    - "couplenet coupling global structure with local parts for object detection"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "focal loss for dense object detection"
    - "me r cnn multi expert region based cnn for object detection"
    - "dssd deconvolutional single shot detector"
    - "deformable convolutional networks"
    - "dsod learning deeply supervised object detectors from scratch"
    - "ron reverse connection with objectness prior networks for object detection"

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)