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: "megdet a large mini batch object detector"
  Abstract: The improvements in recent CNN-based object detection works, from R-CNN [11], Fast/Faster R-CNN [10, 31] to recent Mask R-CNN [14] and RetinaNet [24], mainly come from new network, new framework, or novel loss design. But mini-batch size, a key factor in the training, has not been well studied. In this paper, we propose a Large MiniBatch Object Detector (MegDet) to enable the training with much larger mini-batch size than before (e.g. from 16 to 256), so that we can effectively utilize multiple GPUs (up to 128 in our experiments) to significantly shorten the training time. Technically, we suggest a learning rate policy and Cross-GPU Batch Normalization, which together allow us to successfully train a large mini-batch detector in much less time (e.g., from 33 hours to 4 hours), and achieve even better accuracy. The MegDet is the backbone of our submission (mmAP 52.5%) to COCO 2017 Challenge, where we won the 1st place of Detection task.

Connected through hyperedge HE-1 (induced by source paper "harmonizing transferability and discriminability for adapting object detectors"):
  with members:
    - "contrastive adaptation network for unsupervised domain adaptation"
    - "self training and adversarial background regularization for unsupervised domain adaptive one stage object detection"
    - "unsupervised domain adaptation via regularized conditional alignment"
    - "multi adversarial faster rcnn for unrestricted object detection"
    - "exploring object relation in mean teacher for cross domain detection"
    - "bidirectional learning for domain adaptation of semantic segmentation"
    - "cluster alignment with a teacher for unsupervised domain adaptation"
    - "constructing self motivated pyramid curriculums for cross domain semantic segmentation a non adversarial approach"

Connected through hyperedge HE-2 (induced by source paper "revisiting the sibling head in object detector"):
  with members:
    - "reppoints point set representation for object detection"
    - "double head rcnn rethinking classification and localization for object detection"
    - "gcnet non local networks meet squeeze excitation networks and beyond"
    - "centernet keypoint triplets for object detection"
    - "fcos fully convolutional one stage object detection"
    - "scale aware trident networks for object detection"
    - "bottom up object detection by grouping extreme and center points"
    - "cornernet detecting objects as paired keypoints"

Connected through hyperedge HE-3 (induced by source paper "adapting object detectors with conditional domain normalization"):
  with members:
    - "compound domain adaptation in an open world"
    - "precise synthetic image and lidar presil dataset for autonomous vehicle perception"
    - "bidirectional learning for domain adaptation of semantic segmentation"
    - "domain adaptation for structured output via discriminative patch representations"
    - "semantic image synthesis with spatially adaptive normalization"
    - "diversify and match a domain adaptive representation learning paradigm for object detection"
    - "pointdan a multi scale 3d domain adaption network for point cloud representation"
    - "moment matching for multi source domain adaptation"

Connected through hyperedge HE-4 (induced by source paper "equalization loss for long tailed object recognition"):
  with members:
    - "lvis a dataset for large vocabulary instance segmentation"
    - "decoupling representation and classifier for long tailed recognition"
    - "large scale long tailed recognition in an open world"
    - "class balanced loss based on effective number of samples"
    - "mask scoring r cnn"
    - "meta weight net learning an explicit mapping for sample weighting"
    - "grid r cnn"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"

Connected through hyperedge HE-5 (induced by source paper "spatially adaptive inference with stochastic feature sampling and interpolation"):
  with members:
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "importance estimation for neural network pruning"
    - "centripetal sgd for pruning very deep convolutional networks with complicated structure"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "sbnet sparse blocks network for fast inference"
    - "an end to end approach for speeding up neural network inference"
    - "deformable convnets v2 more deformable better results"

Connected through hyperedge HE-6 (induced by source paper "pbrnet pyramidal bounding box refinement to improve object localization accuracy"):
  with members:
    - "double head rcnn rethinking classification and localization for object detection"
    - "libra r cnn towards balanced learning for object detection"
    - "side aware boundary localization for more precise object detection"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "revisiting rcnn on awakening the classification power of faster rcnn"
    - "path aggregation network for instance segmentation"
    - "focal loss for dense object detection"
    - "attention is all you need"

Connected through hyperedge HE-7 (induced by source paper "towards stabilizing batch statistics in backward propagation of batch normalization"):
  with members:
    - "ssn learning sparse switchable normalization via sparsestmax"
    - "evalnorm estimating batch normalization statistics for evaluation"
    - "rethinking imagenet pre training"
    - "group normalization"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "focal loss for dense object detection"
    - "mask r cnn"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-8 (induced by source paper "efficient scene text detection with textual attention tower"):
  with members:
    - "inceptext a new inception text module with deformable psroi pooling for multi oriented scene text detection"
    - "fots fast oriented text spotting with a unified network"
    - "pixellink detecting scene text via instance segmentation"
    - "textboxes a single shot oriented scene text detector"
    - "east an efficient and accurate scene text detector"
    - "single shot text detector with regional attention"
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "deep direct regression for multi oriented scene text 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)