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: "arcticnet a deep learning solution to classify arctic wetlands"
  Abstract: Arctic environments are rapidly changing under the warming climate. Of particular interest are wetlands, a type of ecosystem that constitutes the most effective terrestrial long-term carbon store. As permafrost thaws, the carbon that was locked in these wetland soils for millennia becomes available for aerobic and anaerobic decomposition, which releases CO2 and CH4, respectively, back to the atmosphere.As CO2 and CH4 are potent greenhouse gases, this transfer of carbon from the land to the atmosphere further contributes to global warming, thereby increasing the rate of permafrost degradation in a positive feedback loop. Therefore, monitoring Arctic wetland health and dynamics is a key scientific task that is also of importance for policy. However, the identification and delineation of these important wetland ecosystems, remain incomplete and often inaccurate. Mapping the extent of Arctic wetlands remains a challenge for the scientific community. Conventional, coarser remote sensing methods are inadequate at distinguishing the diverse and micro-topographically complex non-vascular vegetation that characterize Arctic wetlands, presenting the need for better identification methods. To tackle this challenging problem, we constructed and annotated the first-of-its-kind Arctic Wetland Dataset (AWD). Based on that, we present ArcticNet, a deep neural network that exploits the multi-spectral, high-resolution imagery captured from nanosatellites (Planet Dove CubeSats) with additional DEM from the ArcticDEM project, to semantically label a Arctic study area into six types, in which three Arctic wetland functional types are included. We present multi-fold efforts to handle the arising challenges, including class imbalance, and the choice of fusion strategies. Preliminary results endorse the high promise of ArcticNet, achieving 93.12% in labelling a hold-out set of regions in our Arctic study area.

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)