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: "device placement optimization with reinforcement learning"
  Abstract: The past few years have witnessed a growth in size and computational requirements for training and inference with neural networks. Currently, a common approach to address these requirements is to use a heterogeneous distributed environment with a mixture of hardware devices such as CPUs and GPUs. Importantly, the decision of placing parts of the neural models on devices is often made by human experts based on simple heuristics and intuitions. In this paper, we propose a method which learns to optimize device placement for TensorFlow computational graphs. Key to our method is the use of a sequence-to-sequence model to predict which subsets of operations in a TensorFlow graph should run on which of the available devices. The execution time of the predicted placements is then used as the reward signal to optimize the parameters of the sequence-to-sequence model. Our main result is that on Inception-V3 for ImageNet classification, and on RNN LSTM, for language modeling and neural machine translation, our model finds non-trivial device placements that outperform hand-crafted heuristics and traditional algorithmic methods.

Connected through hyperedge HE-1 (induced by source paper "chameleon adaptive code optimization for expedited deep neural network compilation"):
  with members:
    - "ordering chaos memory aware scheduling of irregularly wired neural networks for edge devices"
    - "reinforced genetic algorithm learning for optimizing computation graphs"
    - "exploring randomly wired neural networks for image recognition"
    - "discovering neural wirings"
    - "active learning for regression using greedy sampling"
    - "meta gradient reinforcement learning"
    - "amc automl for model compression and acceleration on mobile devices"
    - "learning scheduling algorithms for data processing clusters"

Connected through hyperedge HE-2 (induced by source paper "reinforcement learning enhanced quantum inspired algorithm for combinatorial optimization"):
  with members:
    - "solving np hard problems on graphs by reinforcement learning without domain knowledge"
    - "exploratory combinatorial optimization with reinforcement learning"
    - "learning heuristics over large graphs via deep reinforcement learning"
    - "learning to optimize variational quantum circuits to solve combinatorial problems"
    - "ranked reward enabling self play reinforcement learning for combinatorial optimization"
    - "learning to perform local rewriting for combinatorial optimization"
    - "combinatorial optimization with graph convolutional networks and guided tree search"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"

Connected through hyperedge HE-3 (induced by source paper "placement optimization with deep reinforcement learning"):
  with members:
    - "gdp generalized device placement for dataflow graphs"
    - "when to trust your model model based policy optimization"
    - "a comprehensive survey on graph neural networks"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "proximal policy optimization algorithms"
    - "asynchronous methods for deep reinforcement learning"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "deep convolutional networks on graph structured data"

Connected through hyperedge HE-4 (induced by source paper "simulating performance of ml systems with offline profiling"):
  with members:
    - "beyond data and model parallelism for deep neural networks"
    - "exploring hidden dimensions in parallelizing convolutional neural networks"
    - "densely connected convolutional networks"
    - "tensorflow a system for large scale machine learning"
    - "rethinking the inception architecture for computer vision"
    - "one weird trick for parallelizing convolutional neural networks"
    - "one billion word benchmark for measuring progress in statistical language modeling"

Connected through hyperedge HE-5 (induced by source paper "reinforcement learning applications"):
  with members:
    - "coloring big graphs with alphagozero"
    - "learning agile and dynamic motor skills for legged robots"
    - "reinforcement learning for slate based recommender systems a tractable decomposition and practical methodology"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "deep learning based recommender system a survey and new perspectives"
    - "recommendations with negative feedback via pairwise deep reinforcement learning"
    - "deep reinforcement learning for page wise recommendations"
    - "learning dexterous in hand manipulation"

Connected through hyperedge HE-6 (induced by source paper "reinforcement learning and adaptive sampling for optimized dnn compilation"):
  with members:
    - "active learning for regression using greedy sampling"
    - "meta gradient reinforcement learning"
    - "amc automl for model compression and acceleration on mobile devices"
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "learning to optimize tensor programs"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"
    - "efficient neural architecture search via parameter sharing"
    - "deep reinforcement learning that matters"

Connected through hyperedge HE-7 (induced by source paper "accelerated training for cnn distributed deep learning through automatic resource aware layer placement"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "beyond data and model parallelism for deep neural networks"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "parameter hub a rack scale parameter server for distributed deep neural network training"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "pipedream fast and efficient pipeline parallel dnn training"
    - "exploring hidden dimensions in parallelizing convolutional neural networks"
    - "gradient sparsification for communication efficient distributed optimization"

Connected through hyperedge HE-8 (induced by source paper "optimizing multi gpu parallelization strategies for deep learning training"):
  with members:
    - "crossbow scaling deep learning with small batch sizes on multi gpu servers"
    - "findings of the 2016 wmt shared task on cross lingual pronoun prediction"
    - "yet another accelerated sgd resnet 50 training on imagenet in 74 7 seconds"
    - "communication scheduling as a first class citizen in distributed machine learning systems"
    - "scaling neural machine translation"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "three factors influencing minima in sgd"
    - "on scale out deep learning training for cloud and hpc"

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)