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: "weakly supervised 3d pose estimation from a single image using multi view consistency"
  Abstract: We present a novel data-driven regularizer for weakly-supervised learning of 3D#R##N#human pose estimation that eliminates the drift problem that affects existing approaches.#R##N#We do this by moving the stereo reconstruction problem into the loss of the network#R##N#itself. This avoids the need to reconstruct 3D data prior to training and unlike previous#R##N#semi-supervised approaches, avoids the need for a warm-up period of supervised training.#R##N#The conceptual and implementational simplicity of our approach is fundamental to its#R##N#appeal. Not only is it straightforward to augment many weakly-supervised approaches#R##N#with our additional re-projection based loss, but it is obvious how it shapes reconstructions#R##N#and prevents drift. As such we believe it will be a valuable tool for any researcher working#R##N#in weakly-supervised 3D reconstruction. Evaluating on Panoptic, the largest multi-camera#R##N#and markerless dataset available, we obtain an accuracy that is essentially indistinguishable#R##N#from a strongly-supervised approach making full use of 3D groundtruth in training.

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)