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 "generalizable resource allocation in stream processing via deep reinforcement learning"):
  Abstract: This paper considers the problem of resource allocation in stream processing, where continuous data flows must be processed in real time in a large distributed system. To maximize system throughput, the resource allocation strategy that partitions the computation tasks of a stream processing graph onto computing devices must simultaneously balance workload distribution and minimize communication. Since this problem of graph partitioning is known to be NP-complete yet crucial to practical streaming systems, many heuristic-based algorithms have been developed to find reasonably good solutions. In this paper, we present a graph-aware encoder-decoder framework to learn a generalizable resource allocation strategy that can properly distribute computation tasks of stream processing graphs unobserved from training data. We, for the first time, propose to leverage graph embedding to learn the structural information of the stream processing graphs. Jointly trained with the graph-aware decoder using deep reinforcement learning, our approach can effectively find optimized solutions for unseen graphs. Our experiments show that the proposed model outperforms both METIS, a state-of-the-art graph partitioning algorithm, and an LSTM-based encoder-decoder model, in about 70% of the test cases.

Includes members:
  Member "dag gnn dag structure learning with graph neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "causal discovery from incomplete data a deep learning approach")
  Member "learning scheduling algorithms for data processing clusters":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "chameleon adaptive code optimization for expedited deep neural network compilation")
  Member "exploiting rich syntactic information for semantic parsing with graph to sequence model":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "reinforcement learning based graph to sequence model for natural question generation")
  Member "learning multimodal graph to graph translation for molecular optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "analyzing learned molecular representations for property prediction")
  Member "model free control for distributed stream data processing using deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "smart resource management for data streaming using an online bin packing strategy")
  Member "memory augmented policy optimization for program synthesis and semantic parsing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "automl zero evolving machine learning algorithms from scratch")
  Member "junction tree variational autoencoder for molecular graph generation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "benchmarking graph neural networks")
  Member "progressive neural architecture search":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "gan compression efficient architectures for interactive conditional gans")

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.AI(Artificial Intelligence)