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 "learning memory access patterns"):
  Abstract: The explosion in workload complexity and the recent slow-down in Moore's law scaling call for new approaches towards efficient computing. Researchers are now beginning to use recent advances in machine learning in software optimizations, augmenting or replacing traditional heuristics and data structures. However, the space of machine learning for computer hardware architecture is only lightly explored. In this paper, we demonstrate the potential of deep learning to address the von Neumann bottleneck of memory performance. We focus on the critical problem of learning memory access patterns, with the goal of constructing accurate and efficient memory prefetchers. We relate contemporary prefetching strategies to n-gram models in natural language processing, and show how recurrent neural networks can serve as a drop-in replacement. On a suite of challenging benchmark datasets, we find that neural networks consistently demonstrate superior performance in terms of precision and recall. This work represents the first step towards practical neural-network based prefetching, and opens a wide range of exciting directions for machine learning in computer architecture research.

Includes members:
  Member "beyond word importance contextual decomposition to extract interactions from lstms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "understanding image captioning models beyond visualizing attention")
  Member "the case for learned index structures":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "monotonic cardinality estimation of similarity selection a deep learning approach")
  Member "automatic rule extraction from long short term memory networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "chexplain enabling physicians to explore and understanddata driven ai enabled medical imaging analysis")
  Member "wavenet a generative model for raw audio":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning")
  Member "pixel recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mast a memory augmented self supervised tracker")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "neural turing machines":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mast a memory augmented self supervised tracker")
  Member "learning to execute":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "curriculum by texture")

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.LG(Machine Learning)