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: "deepbox learning objectness with convolutional networks"
  Abstract: Existing object proposal approaches use primarily bottom-up cues to rank proposals, while we believe that objectness is in fact a high level construct. We argue for a data-driven, semantic approach for ranking object proposals. Our framework, which we call DeepBox, uses convolutional neural networks (CNNs) to rerank proposals from a bottom-up method. We use a novel four-layer CNN architecture that is as good as much larger networks on the task of evaluating objectness while being much faster. We show that DeepBox significantly improves over the bottom-up ranking, achieving the same recall with 500 proposals as achieved by bottom-up methods with 2000. This improvement generalizes to categories the CNN has never seen before and leads to a 4.5-point gain in detection mAP. Our implementation achieves this performance while running at 260 ms per image.

Connected through hyperedge HE-1 (induced by source paper "scale matters temporal scale aggregation network for precise action localization in untrimmed videos"):
  with members:
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "segregated temporal assembly recurrent networks for weakly supervised multiple action detection"
    - "cascaded pyramid mining network for weakly supervised temporal action localization"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "s3d single shot multi span detector via fully 3d convolutional networks"
    - "end to end dense video captioning with masked transformer"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "ctap complementary temporal action proposal generation"

Connected through hyperedge HE-2 (induced by source paper "deep neural networks for marine debris detection in sonar images"):
  with members:
    - "on the convergence of adam and beyond"
    - "taking human out of learning applications a survey on automated machine learning"
    - "darts differentiable architecture search"
    - "towards understanding regularization in batch normalization"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "multi task learning using uncertainty to weigh losses for scene geometry and semantics"
    - "learning transferable architectures for scalable image recognition"
    - "dsod learning deeply supervised object detectors from scratch"

Connected through hyperedge HE-3 (induced by source paper "clouds of oriented gradients for 3d detection of objects surfaces and indoor scene layouts"):
  with members:
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "focal loss for dense object detection"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "roomnet end to end room layout estimation"
    - "semantic scene completion from a single depth image"
    - "3d object proposals using stereo imagery for accurate object class detection"

Connected through hyperedge HE-4 (induced by source paper "harvesting visual objects from internet images via deep learning based objectness assessment"):
  with members:
    - "contrast oriented deep neural networks for salient object detection"
    - "deepqoe a unified framework for learning to predict video qoe"
    - "instance level salient object segmentation"
    - "exploiting web images for dataset construction a domain robust approach"
    - "quasi homography warps in image stitching"
    - "instance sensitive fully convolutional networks"
    - "convolutional oriented boundaries"
    - "salient object subitizing"

Connected through hyperedge HE-5 (induced by source paper "bsn boundary sensitive network for temporal action proposal generation"):
  with members:
    - "soft nms improving object detection with one line of code"
    - "activitynet challenge 2017 summary"
    - "single shot temporal action detection"
    - "temporal action detection with structured segment networks"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "cascaded boundary regression for temporal action detection"
    - "temporal context network for activity localization in videos"
    - "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos"

Connected through hyperedge HE-6 (induced by source paper "segmenting unknown 3d objects from real depth images using mask r cnn trained on synthetic data"):
  with members:
    - "clusternet instance segmentation in rgb d images"
    - "rgb d object detection and semantic segmentation for autonomous manipulation in clutter"
    - "sgpn similarity group proposal network for 3d point cloud instance segmentation"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "cartman the low cost cartesian manipulator that won the amazon robotics challenge"
    - "semantic segmentation from limited training data"
    - "end to end learning of semantic grasping"
    - "dex net 3 0 computing robust robot suction grasp targets in point clouds using a new analytic model and deep learning"

Connected through hyperedge HE-7 (induced by source paper "toward scale invariance and position sensitive region proposal networks"):
  with members:
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "training region based object detectors with online hard example mining"
    - "hypernet towards accurate region proposal generation and joint object detection"
    - "learning to refine object segments"
    - "what makes for effective detection proposals"
    - "feature pyramid networks for object detection"
    - "r fcn object detection via region based fully convolutional networks"
    - "object proposal evaluation protocol is gameable"

Connected through hyperedge HE-8 (induced by source paper "mixed supervised object detection with robust objectness transfer"):
  with members:
    - "weakly supervised object localization using things and stuff transfer"
    - "transfer learning by ranking for weakly supervised object annotation"
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "improving weakly supervised object localization by micro annotation"
    - "training region based object detectors with online hard example mining"
    - "weakly supervised cascaded convolutional networks"
    - "contextlocnet context aware deep network models for weakly supervised localization"
    - "r fcn object detection via region based fully convolutional 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)