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: "craft objects from images"
  Abstract: Object detection is a fundamental problem in image understanding. One popular solution is the R-CNN framework and its fast versions. They decompose the object detection problem into two cascaded easier tasks: 1) generating object proposals from images, 2) classifying proposals into various object categories. Despite that we are handling with two relatively easier tasks, they are not solved perfectly and there's still room for improvement. In this paper, we push the "divide and conquer" solution even further by dividing each task into two sub-tasks. We call the proposed method "CRAFT" (Cascade Region-proposal-network And FasT-rcnn), which tackles each task with a carefully designed network cascade. We show that the cascade structure helps in both tasks: in proposal generation, it provides more compact and better localized object proposals; in object classification, it reduces false positives (mainly between ambiguous categories) by capturing both inter- and intra-category variances. CRAFT achieves consistent and considerable improvement over the state-of-the-art on object detection benchmarks like PASCAL VOC 07/12 and ILSVRC.

Connected through hyperedge HE-1 (induced by source paper "relation distillation networks for video object detection"):
  with members:
    - "exploring object relation in mean teacher for cross domain detection"
    - "cornernet detecting objects as paired keypoints"
    - "object detection in video with spatiotemporal sampling networks"
    - "videos as space time region graphs"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "exploring visual relationship for image captioning"
    - "non local neural networks"
    - "towards high performance video object detection"

Connected through hyperedge HE-2 (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-3 (induced by source paper "hybrid task cascade for instance segmentation"):
  with members:
    - "libra r cnn towards balanced learning for object detection"
    - "region proposal by guided anchoring"
    - "fishnet a versatile backbone for image region and pixel level prediction"
    - "acquisition of localization confidence for accurate object detection"
    - "path aggregation network for instance segmentation"
    - "focal loss for dense object detection"
    - "cascade r cnn delving into high quality object detection"
    - "deformable convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "c rpns promoting object detection in real world via a cascade structure of region proposal networks"):
  with members:
    - "hybrid task cascade for instance segmentation"
    - "siamese cascaded region proposal networks for real time visual tracking"
    - "structure inference net object detection using scene level context and instance level relationships"
    - "focal loss for dense object detection"
    - "citypersons a diverse dataset for pedestrian detection"
    - "cascade r cnn delving into high quality object detection"
    - "dssd deconvolutional single shot detector"
    - "deformable convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "learning to separate detecting heavily occluded objects in urban scenes"):
  with members:
    - "double anchor r cnn for human detection in a crowd"
    - "learning a layout transfer network for context aware object detection"
    - "sinet a scale insensitive convolutional neural network for fast vehicle detection"
    - "mask guided attention network for occluded pedestrian detection"
    - "adaptive nms refining pedestrian detection in a crowd"
    - "cornernet detecting objects as paired keypoints"
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "bounding box regression with uncertainty for accurate object detection"

Connected through hyperedge HE-6 (induced by source paper "cascade rpn delving into high quality region proposal network with adaptive convolution"):
  with members:
    - "region proposal by guided anchoring"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "feature selective anchor free module for single shot object detection"
    - "hybrid task cascade for instance segmentation"
    - "fcos fully convolutional one stage object detection"
    - "cornernet detecting objects as paired keypoints"
    - "siamese cascaded region proposal networks for real time visual tracking"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-7 (induced by source paper "region proposal by guided anchoring"):
  with members:
    - "hybrid task cascade for instance segmentation"
    - "metaanchor learning to detect objects with customized anchors"
    - "single shot bidirectional pyramid networks for high quality object detection"
    - "toward scale invariance and position sensitive region proposal networks"
    - "beyond trade off accelerate fcn based face detector with higher accuracy"
    - "improving object localization with fitness nms and bounded iou loss"
    - "focal loss for dense object detection"
    - "cascade r cnn delving into high quality object detection"

Connected through hyperedge HE-8 (induced by source paper "residual objectness for imbalance reduction"):
  with members:
    - "foveabox beyond anchor based object detector"
    - "libra r cnn towards balanced learning for object detection"
    - "region proposal by guided anchoring"
    - "objects as points"
    - "bottom up object detection by grouping extreme and center points"
    - "cornernet detecting objects as paired keypoints"
    - "siamese cascaded region proposal networks for real time visual tracking"
    - "yolov3 an incremental improvement"

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)