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: "a survey on compiler autotuning using machine learning"
  Abstract: Since the mid-1990s, researchers have been trying to use machine-learning-based approaches to solve a number of different compiler optimization problems. These techniques primarily enhance the quality of the obtained results and, more importantly, make it feasible to tackle two main compiler optimization problems: optimization selection (choosing which optimizations to apply) and phase-ordering (choosing the order of applying optimizations). The compiler optimization space continues to grow due to the advancement of applications, increasing number of compiler optimizations, and new target architectures. Generic optimization passes in compilers cannot fully leverage newly introduced optimizations and, therefore, cannot keep up with the pace of increasing options. This survey summarizes and classifies the recent advances in using machine learning for the compiler optimization field, particularly on the two major problems of (1) selecting the best optimizations, and (2) the phase-ordering of optimizations. The survey highlights the approaches taken so far, the obtained results, the fine-grain classification among different approaches, and finally, the influential papers of the field.

Connected through hyperedge HE-1 (induced by source paper "a view on deep reinforcement learning in system optimization"):
  with members:
    - "neural packet classification"
    - "neo a learned query optimizer"
    - "reinforced genetic algorithm learning for optimizing computation graphs"
    - "placeto learning generalizable device placement algorithms for distributed machine learning"
    - "dopamine a research framework for deep reinforcement learning"
    - "iroko a framework to prototype reinforcement learning for data center traffic control"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "learning state representations for query optimization with deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "benanza automatic mu benchmark generation to compute lower bound latency and inform optimizations of deep learning models on gpus"):
  with members:
    - "miopen an open source library for deep learning primitives"
    - "a modular benchmarking infrastructure for high performance and reproducible deep learning"
    - "xsp across stack profiling and analysis of machine learning models on gpus"
    - "neurovectorizer end to end vectorization with deep reinforcement learning"
    - "analysis of dawnbench a time to accuracy machine learning performance benchmark"
    - "arcface additive angular margin loss for deep face recognition"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-3 (induced by source paper "using structured input and modularity for improved learning"):
  with members:
    - "using meta heuristics and machine learning for software optimization of parallel computing systems a systematic literature review"
    - "compositional attention networks for machine reasoning"
    - "routing networks adaptive selection of non linear functions for multi task learning"
    - "dynamic routing between capsules"
    - "learning to infer graphics programs from hand drawn images"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "learning to compose neural networks for question answering"
    - "google s neural machine translation system bridging the gap between human and machine translation"

Connected through hyperedge HE-4 (induced by source paper "neurovectorizer end to end vectorization with deep reinforcement learning"):
  with members:
    - "a view on deep reinforcement learning in system optimization"
    - "code2vec learning distributed representations of code"
    - "machine learning in compiler optimisation"
    - "tune a research platform for distributed model selection and training"
    - "ray a distributed framework for emerging ai applications"
    - "rllib abstractions for distributed reinforcement learning"
    - "proximal policy optimization algorithms"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "provably efficient q learning with low switching cost"):
  with members:
    - "learning to optimize join queries with deep reinforcement learning"
    - "policy certificates towards accountable reinforcement learning"
    - "device placement optimization with reinforcement learning"
    - "online learning with switching costs and other adaptive adversaries"

Connected through hyperedge HE-6 (induced by source paper "lost in translation exposing hidden compiler optimization opportunities"):
  with members:
    - "the iot energy challenge a software perspective"
    - "less is more exploiting the standard compiler optimization levels for better performance and energy consumption"
    - "a collective knowledge workflow for collaborative research into multi objective autotuning and machine learning techniques"
    - "entra whole systems energy transparency"

Connected through hyperedge HE-7 (induced by source paper "fast on the fly retraining free sparsification of convolutional neural networks"):
  with members:
    - "learning to prune filters in convolutional neural networks"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "exploring the regularity of sparse structure in convolutional neural networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "is input sparsity time possible for kernel low rank approximation"
    - "learning to prune deep neural networks via layer wise optimal brain surgeon"
    - "learning structured sparsity in deep neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"

Connected through hyperedge HE-8 (induced by source paper "less is more exploiting the standard compiler optimization levels for better performance and energy consumption"):
  with members:
    - "the iot energy challenge a software perspective"
    - "energy transparency for deeply embedded programs"
    - "entra whole systems energy transparency"
    - "identifying compiler options to minimize energy consumption for embedded platforms"
    - "static analysis of energy consumption for llvm ir programs"
    - "beebs open benchmarks for energy measurements on embedded platforms"

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.PL(Programming Languages)