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: "is sampling heuristics necessary in training deep object detectors"
  Abstract: To address the imbalance between foreground and background, various heuristic methods, such as OHEM, Focal Loss, GHM, have been proposed for biased sampling or weighting when training deep object detectors. We challenge this paradigm by discarding the sampling heuristics and focusing on other settings for training. Our empirical study reveals that the weight of classification loss and the initialization strategy have a big impact on the training stability and the final accuracy. Thus, we propose the \emph{Sampling-Free} mechanism, including three key ingredients: optimal bias initialization, guided loss weights, and class-adaptive threshold, for training deep detectors without sampling heuristics. Compared with the sampling heuristics, our Sampling-Free mechanism is fully data diagnostic and thus avoids the laborious tuning of sampling hyper-parameters. Our extensive experimental results demonstrate that the Sampling-Free mechanism can be used for one-stage, two-stage, and anchor-free object detectors, where it always achieves higher accuracy on the challenging COCO benchmark. The mechanism is also useful for the instance segmentation task. Code is at this https URL.

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)