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 "agl a scalable system for industrial purpose graph machine learning"):
  Abstract: Machine learning over graphs have been emerging as powerful learning tools for graph data. However, it is challenging for industrial communities to leverage the techniques, such as graph neural networks (GNNs), and solve real-world problems at scale because of inherent data dependency in the graphs. As such, we cannot simply train a GNN with classic learning systems, for instance parameter server that assumes data parallel. Existing systems store the graph data in-memory for fast accesses either in a single machine or graph stores from remote. The major drawbacks are in three-fold. First, they cannot scale because of the limitations on the volume of the memory, or the bandwidth between graph stores and workers. Second, they require extra development of graph stores without well exploiting mature infrastructures such as MapReduce that guarantee good system properties. Third, they focus on training but ignore the optimization of inference over graphs, thus makes them an unintegrated system. #R##N#In this paper, we design AGL, a scalable, fault-tolerance and integrated system, with fully-functional training and inference for GNNs. Our system design follows the message passing scheme underlying the computations of GNNs. We design to generate the $k$-hop neighborhood, an information-complete subgraph for each node, as well as do the inference simply by merging values from in-edge neighbors and propagating values to out-edge neighbors via MapReduce. In addition, the $k$-hop neighborhood contains information-complete subgraphs for each node, thus we simply do the training on parameter servers due to data independency. Our system AGL, implemented on mature infrastructures, can finish the training of a 2-layer graph attention network on a graph with billions of nodes and hundred billions of edges in 14 hours, and complete the inference in 1.2 hour.

Includes members:
  Member "dsslp a distributed framework for semi supervised link prediction":
    also connected through other hyperedges:
      (none)
  Member "heterogeneous graph neural networks for malicious account detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey of adversarial learning on graphs")
  Member "fast graph representation learning with pytorch geometric":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on feature normalization and data augmentation")
  Member "pytorch biggraph a large scale graph embedding system":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "interpretable and fair comparison of link prediction or entity alignment methods with adjusted mean rank")
  Member "graph convolutional neural networks for web scale recommender systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on the expressive power of graph neural networks")
  Member "sentence state lstm for text representation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gret global representation enhanced transformer")
  Member "predicting multicellular function through multi layer tissue networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "self supervised graph representation learning via global context prediction")
  Member "semi supervised classification with graph convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")

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.SI(Social and Information Networks)