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: "uwb gcn accelerating graph convolutional networks through runtime workload rebalancing"
  Abstract: Deep learning systems have been applied mostly to Euclidean data such as images, video, and audio. In many applications, however, information and their relationships are better expressed with graphs. Graph convolutional networks (GCNs) appear to be a promising approach to efficiently learn from graph data structures, having shown advantages in many critical applications such as power system analysis, chemical reactivity prediction, material property prediction, E-commerce, cybersecurity, etc. As with other deep learning modalities, hardware acceleration is critical. The challenge is that real-world graphs are often extremely large and unbalanced; this poses both significant performance demands and design challenges.  We propose an architecture that accelerates GCN inference, the Ultra Workload Balanced GCN (UWB-GCN). To address the major performance bottleneck of workload imbalance we propose two techniques, dynamic local sharing and dynamic remote switching. Both rely on hardware flexibility to autotune the system; this is effected with the negligible area and delay overhead. In particular, UWB-GCN profiles the sparse graph pattern while continuously adjusting the workload distribution strategy among a large number of processing elements (PEs). Once the system converges to an ideal configuration, this configuration is used for the remaining iterations. To the best of our knowledge, UWB-GCN is the first accelerator design targeting GCNs and the first that autotunes workload balance in the accelerator in hardware rather than software. These methods result in a near-ideal workload balance in processing sparse matrices. Experimental results show that UWB-GCN can perform inference of the Nell graph (66K vertices, 266K edges) in 8.1 ms; this corresponds to speedups of 199x, 16x, and 7.5x as compared with, respectively, CPU, GPU, and a baseline design with no workload rebalancing.

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.DC(Distributed, Parallel, and Cluster Computing)