human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "mixture model based bounding box density estimation for object detection"):
  Abstract: In this paper, we propose a new object detection model, Mixture-Model-based Object Detector (MMOD), that performs multi-object detection using a mixture model. Unlike previous studies, we use density estimation to deal with the multi-object detection task. MMOD captures the conditional distribution of bounding boxes for a given input image using a mixture model consisting of Gaussian and categorical distributions. For this purpose, we propose a method to extract object bounding boxes from a trained mixture model. In doing so, we also propose a new network structure and objective function for the MMOD. Our proposed method is not trained by assigning a ground truth bounding box to a specific location on the network's output. Instead, the mixture components are automatically learned to represent the distribution of the bounding box through density estimation. Therefore, MMOD does not require a large number of anchors and does not incur the positive-negative imbalance problem. This not only benefits the detection performance but also enhances the inference speed without requiring additional processing. We applied MMOD to Pascal VOC and MS COCO datasets, and outperform the detection performance with inference speed of other state-of-the-art fast object detection methods. (38.7 AP with 39ms per image on MS COCO without bells and whistles.) Code will be available.

Includes members:
  Member "reppoints point set representation for object detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "udd an underwater open sea farm object detection dataset for underwater robot picking")
  Member "foveabox beyond anchor based object detector":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "udd an underwater open sea farm object detection dataset for underwater robot picking")
  Member "libra r cnn towards balanced learning for object detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "forward and backward information retention for accurate binary neural networks")
  Member "centernet keypoint triplets for object detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "revisiting the sibling head in object detector")
  Member "region proposal by guided anchoring":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "udd an underwater open sea farm object detection dataset for underwater robot picking")
  Member "revisiting feature alignment for one stage object detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ehsod cam guided end to end hybrid supervised object detection with cascade refinement")
  Member "towards accurate one stage object detection with ap loss":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "bridging the gap between anchor based and anchor free detection via adaptive training sample selection")
  Member "gaussian yolov3 an accurate and fast object detector using localization uncertainty for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "monopair monocular 3d object detection using pairwise spatial relationships")

Question: Please tell me which class the source paper 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)