human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "graphblast a high performance linear algebra based graph framework on the gpu":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "jgrapht a java library for graph data structures and algorithms")
  Member "a comprehensive survey on graph neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on the expressive power of graph neural networks")
  Member "packing sparse convolutional neural networks for efficient systolic array implementations column combining under joint optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "full stack optimization for accelerating cnns with fpga validation")
  Member "graph neural networks a review of methods and applications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "natural language qa approaches using reasoning with external knowledge")
  Member "graph convolutional neural networks for web scale recommender systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on the expressive power of graph neural networks")
  Member "deeper insights into graph convolutional networks for semi supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "memory based graph networks")
  Member "modeling polypharmacy side effects with graph convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "differentiable graph module dgm graph convolutional networks")
  Member "large scale learnable graph convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "transferring robustness for graph neural network against poisoning attacks")

Question: Please tell me which class the source paper 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)