human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "learned indexes for dynamic workloads"):
  with members:
    - "a model for learned bloom filters and optimizing by sandwiching"
    - "learned cardinalities estimating correlated joins with deep learning"
    - "learning state representations for query optimization with deep reinforcement learning"
    - "deep reinforcement learning for join order enumeration"
    - "regularized evolution for image classifier architecture search"
    - "learning to optimize join queries with deep reinforcement learning"
    - "quicksel quick selectivity learning with mixture models"
    - "an o n sorting algorithm machine learning sorting"

Connected through hyperedge HE-2 (induced by source paper "learning execution through neural code fusion"):
  with members:
    - "a neural network prefetcher for arbitrary memory access patterns"
    - "neural code comprehension a learnable representation of code semantics"
    - "path based function embedding and its application to specification mining"
    - "code vectors understanding programs through embedded abstracted symbolic traces"
    - "ithemal accurate portable and fast basic block throughput estimation using deep neural networks"
    - "neural arithmetic logic units"
    - "neural guided deductive search for real time program synthesis from examples"
    - "automatic program synthesis of long programs with a learned garbage collector"

Connected through hyperedge HE-3 (induced by source paper "a neural network prefetcher for arbitrary memory access patterns"):
  with members:
    - "in datacenter performance analysis of a tensor processing unit"
    - "lstm a search space odyssey"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "bitwise neural networks"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "ternary neural networks for resource efficient ai applications"
    - "quantized neural networks training neural networks with low precision weights and activations"
    - "neural random access machines"

Connected through hyperedge HE-4 (induced by source paper "smartchoices hybridizing programming and machine learning"):
  with members:
    - "playing hard exploration games by watching youtube"
    - "competitive caching with machine learned advice"
    - "addressing function approximation error in actor critic methods"
    - "kickstarting deep reinforcement learning"
    - "the case for learned index structures"
    - "a deep reinforcement learning based framework for content caching"
    - "neural combinatorial optimization with reinforcement learning"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "alex an updatable adaptive learned index"):
  with members:
    - "learned cardinalities estimating correlated joins with deep learning"
    - "learning state representations for query optimization with deep reinforcement learning"
    - "learning to optimize join queries with deep reinforcement learning"
    - "a tree a bounded approximate index structure"
    - "towards a hands free query optimizer through deep learning"
    - "the case for learned index structures"
    - "insertion sort is o n log n"

Connected through hyperedge HE-6 (induced by source paper "ithemal accurate portable and fast basic block throughput estimation using deep neural networks"):
  with members:
    - "automated instruction stream throughput prediction for intel and amd microarchitectures"
    - "cross sentence n ary relation extraction with graph lstms"
    - "neural network based graph embedding for cross platform binary code similarity detection"
    - "linguistic knowledge as memory for recurrent neural networks"
    - "learning to represent programs with graphs"
    - "hierarchical boundary aware neural encoder for video captioning"
    - "hierarchical multiscale recurrent neural networks"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-7 (induced by source paper "feedforward neural networks for caching enough or too much"):
  with members:
    - "using grouped linear prediction and accelerated reinforcement learning for online content caching"
    - "multi agent learning for cooperative large scale caching networks"
    - "deep reinforcement learning that matters"
    - "a deep reinforcement learning based framework for content caching"
    - "overcoming catastrophic forgetting in neural networks"

Question: Please tell me which class the center node 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)