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: "fchd a fast and accurate head detector"
  Abstract: In this paper, we propose FCHD-Fully Convolutional Head Detector, which is an end-to-end trainable head detection model, which runs at 5 fps and with 0.70 average precision (AP), on a very modest GPU. Recent head detection techniques have avoided using anchors as a starting point for detection especially in the cases where the detection has to happen in the wild. The reason is poor performance of anchor-based techniques under scenarios where the object size is small. We argue that a good AP can be obtained with carefully designed anchors, where the anchor design choices are made based on the receptive field size of the hidden layers. Our contribution is two folds. 1) A simple fully convolutional anchor based model which is end-to-end trainable and has a very low inference time. 2) Carefully chosen anchor sizes which play a key role in getting good average precision. Our model achieves comparable results than many other baselines on challenging head detection dataset like BRAINWASH. Along with accuracy, our model has least runtime among all the baselines along with modest hardware requirements which makes it suitable for edge deployments in surveillance applications. The code is made open-source at https://github.com/aditya-vora/FCHD-Fully-Convolutional-Head-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)