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: "pose neural fabrics search"
  Abstract: Neural Architecture Search (NAS) technologies have been successfully performed for efficient neural architectures for tasks such as image classification and semantic segmentation. However, existing works implement NAS for target tasks independently of domain knowledge and focus only on searching for an architecture to replace the human-designed network in a common pipeline. \emph{Can we exploit human prior knowledge to guide NAS?} To address it, we propose a framework, named Pose Neural Fabrics Search (PNFS), introducing prior knowledge of body structure into NAS for human pose estimation. We lead a new neural architecture search space, by parameterizing Cell-based Neural Fabric, to learn micro as well as macro neural architecture using a differentiable search strategy. To take advantage of part-based structural knowledge of the human body and learning capability of NAS, global pose constraint relationships are modeled as multiple part representations, each of which is predicted by a personalized Cell-based Neural Fabric. In part representation, we view human skeleton keypoints as entities by representing them as vectors at image locations, expecting it to capture keypoint's feature in a relaxed vector space. The experiments on MPII and MS-COCO datasets demonstrate that PNFS\footnote{Code is available at \url{https://github.com/yangsenius/PoseNFS}} can achieve comparable performance to state-of-the-art methods, with fewer parameters and lower computational complexity.

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)