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: "pyramidal edge maps based guided thermal super resolution"
  Abstract: Thermal imaging is a robust sensing technique but its consumer applicability is limited by the high cost of thermal sensors. Nevertheless, low-resolution thermal cameras are relatively affordable and are also usually accompanied by a high-resolution visible-range camera. This visible-range image can be used as a guide to reconstruct a high-resolution thermal image using guided super-resolution(GSR) techniques. However, the difference in wavelength-range of the input images makes this task challenging. Improper processing can introduce artifacts such as blur and ghosting, mainly due to texture and content mismatch. To this end, we propose a novel algorithm for guided super-resolution that explicitly tackles the issue of texture-mismatch caused due to multimodality. We propose a two-stage network that combines information from a low-resolution thermal and a high-resolution visible image with the help of multi-level edge-extraction and integration. The first stage of our network extracts edge-maps from the visual image at different pyramidal levels and the second stage integrates these edge-maps into our proposed super-resolution network at appropriate layers. Extraction and integration of edges belonging to different scales simplifies the task of GSR as it provides texture to object-level information in a progressive manner. Using multi-level edges also allows us to adjust the contribution of the visual image directly at the time of testing and thus provides controllability at test-time. We perform multiple experiments and show that our method performs better than existing state-of-the-art guided super-resolution methods both quantitatively and qualitatively.

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)