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 "engn a high throughput and energy efficient accelerator for large graph neural networks"):
  Abstract: Inspired by the great success of convolutional neural networks on structural data like videos and images, graph neural network (GNN) emerges as a powerful approach to process non-euclidean data structures and has been proved powerful in various application domains such as social network, e-commerce, and knowledge graph. However, such graph data maintained in IT companies can be extremely large and sparse, thus employing GNNs to deal with them requires substantial computational power and memory bandwidth, which induces the considerable energy and resources cost spent on general-purpose CPUs and GPUs. In addition, GNN operating on irregular graphs can hardly be fitted to the conventional neural network accelerators or graph processors, which are not designed to support the computation abstraction of GNNs. #R##N#This work presents a specialized accelerator architecture, EnGN, to enable high-throughput and energy-efficient processing of large-scale graph neural networks. The proposed EnGN is designed to accelerate the three key stages of GNN propagation, which is abstracted as common computing patterns shared by typical GNNs. To support the key stages simultaneously, we propose the ring-edge-reduce(RER) dataflow that tames the poor locality of sparsely-and-randomly connected vertices, and the RER PE-array to practice RER dataflow. In addition, we utilize a graph tiling strategy to fit large graphs into EnGN and make the best use of the hierarchical on-chip buffers through adaptive computation reordering and tile scheduling. The experiments on representative GNN models with the input of realistic graphs shows that EnGN achieves 303.45x and 4.44x performance speedup while consuming 1370.52x and 93.73x less energy on average when compared to the CPU and GPU baselines empowered by the state-of-the-art software frameworks, respectively.

Includes members:
  Member "bandwidth reduction using importance weighted pruning on ring allreduce":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "distributed low precision training without mixed precision")
  Member "cross lingual knowledge graph alignment via graph matching neural network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep graph matching consensus")
  Member "a comprehensive survey on graph neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on the expressive power of graph neural networks")
  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 "adaptive sampling towards fast graph representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "spectral graph attention network")
  Member "fastgcn fast learning with graph convolutional networks via importance sampling":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "spectral graph attention network")
  Member "how powerful are graph neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on the expressive power of graph neural networks")
  Member "recent advances in recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "fundamentals of recurrent neural network rnn and long short term memory lstm network")

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)