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: "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.

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)