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: "in bed pressure based pose estimation using image space representation learning"
  Abstract: In-bed pose estimation has shown value in fields such as hospital patient monitoring, sleep studies, and smart homes. In this paper, we present a novel in-bed pressure-based pose estimation approach capable of accurately detecting body parts from highly ambiguous pressure data. We exploit the idea of using a learnable pre-processing step, which transforms the vague pressure maps to a representation close to the expected input space of common purpose pose identification modules, which fail if solely used on the pressure data. To this end, a fully convolutional network with multiple scales is used as the learnable pre-processing step to provide the pose-specific characteristics of the pressure maps to the pre-trained pose identification module. A combination of loss functions is used to model the constraints, ensuring that unclear body parts are reconstructed correctly while preventing the pre-processing block from generating arbitrary images. The evaluation results show high visual fidelity in the generated pre-processed images as well as high detection rates in pose estimation. Furthermore, we show that the trained pre-processing block can be effective for pose identification models for which it has not been trained as well.

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)