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: "revisiting feature alignment for one stage object detection"
  Abstract: Recently, one-stage object detectors gain much attention due to their simplicity in practice. Its fully convolutional nature greatly reduces the difficulty of training and deployment compared with two-stage detectors which require NMS and sorting for the proposal stage. However, a fundamental issue lies in all one-stage detectors is the misalignment between anchor boxes and convolutional features, which significantly hinders the performance of one-stage detectors. In this work, we first reveal the deep connection between the widely used im2col operator and the RoIAlign operator. Guided by this illuminating observation, we propose a RoIConv operator which aligns the features and its corresponding anchors in one-stage detection in a principled way. We then design a fully convolutional AlignDet architecture which combines the flexibility of learned anchors and the preciseness of aligned features. Specifically, our AlignDet achieves a state-of-the-art mAP of 44.1 on the COCO test-dev with ResNeXt-101 backbone.

Connected through hyperedge HE-1 (induced by source paper "ehsod cam guided end to end hybrid supervised object detection with cascade refinement"):
  with members:
    - "baod budget aware object detection"
    - "weakly supervised object detection with segmentation collaboration"
    - "object aware instance labeling for weakly supervised object detection"
    - "region proposal by guided anchoring"
    - "min entropy latent model for weakly supervised object detection"
    - "feature selective anchor free module for single shot object detection"
    - "c mil continuation multiple instance learning for weakly supervised object detection"
    - "rethinking imagenet pre training"

Connected through hyperedge HE-2 (induced by source paper "mixture model based bounding box density estimation for object detection"):
  with members:
    - "reppoints point set representation for object detection"
    - "foveabox beyond anchor based object detector"
    - "libra r cnn towards balanced learning for object detection"
    - "centernet keypoint triplets for object detection"
    - "region proposal by guided anchoring"
    - "towards accurate one stage object detection with ap loss"
    - "gaussian yolov3 an accurate and fast object detector using localization uncertainty for autonomous driving"
    - "feature selective anchor free module for single shot object detection"

Connected through hyperedge HE-3 (induced by source paper "sm nas structural to modular neural architecture search for object detection"):
  with members:
    - "thundernet towards real time generic object detection"
    - "region proposal by guided anchoring"
    - "detnas backbone search for object detection"
    - "partial order pruning for best speed accuracy trade off in neural architecture search"
    - "nas fcos fast neural architecture search for object detection"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "feature selective anchor free module for single shot object detection"
    - "scale aware trident networks for object detection"

Connected through hyperedge HE-4 (induced by source paper "iou aware single stage object detector for accurate localization"):
  with members:
    - "libra r cnn towards balanced learning for object detection"
    - "polarmask single shot instance segmentation with polar representation"
    - "iou balanced loss functions for single stage object detection"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "fcos fully convolutional one stage object detection"
    - "prime sample attention in object detection"
    - "yolov3 an incremental improvement"
    - "gradient harmonized single stage detector"

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)