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: "dynamic control flow in large scale machine learning"
  Abstract: Many recent machine learning models rely on fine-grained dynamic control flow for training and inference. In particular, models based on recurrent neural networks and on reinforcement learning depend on recurrence relations, data-dependent conditional execution, and other features that call for dynamic control flow. These applications benefit from the ability to make rapid control-flow decisions across a set of computing devices in a distributed system. For performance, scalability, and expressiveness, a machine learning system must support dynamic control flow in distributed and heterogeneous environments.   This paper presents a programming model for distributed machine learning that supports dynamic control flow. We describe the design of the programming model, and its implementation in TensorFlow, a distributed machine learning system. Our approach extends the use of dataflow graphs to represent machine learning models, offering several distinctive features. First, the branches of conditionals and bodies of loops can be partitioned across many machines to run on a set of heterogeneous devices, including CPUs, GPUs, and custom ASICs. Second, programs written in our model support automatic differentiation and distributed gradient computations, which are necessary for training machine learning models that use control flow. Third, our choice of non-strict semantics enables multiple loop iterations to execute in parallel across machines, and to overlap compute and I/O operations.   We have done our work in the context of TensorFlow, and it has been used extensively in research and production. We evaluate it using several real-world applications, and demonstrate its performance and scalability.

Connected through hyperedge HE-1 (induced by source paper "a simple differentiable programming language"):
  with members:
    - "pytorch an imperative style high performance deep learning library"
    - "efficient differentiable programming in a functional array processing language"
    - "demystifying differentiable programming shift reset the penultimate backpropagator"
    - "the simple essence of automatic differentiation"
    - "tangent automatic differentiation using source code transformation for dynamically typed array programming"
    - "developing bug free machine learning systems with formal mathematics"
    - "tricks from deep learning"
    - "a convenient differential category"

Connected through hyperedge HE-2 (induced by source paper "reinforced genetic algorithm learning for optimizing computation graphs"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "machine learning for combinatorial optimization a methodological tour d horizon"
    - "beyond data and model parallelism for deep neural networks"
    - "learning scheduling algorithms for data processing clusters"
    - "glow graph lowering compiler techniques for neural networks"
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "learning to optimize tensor programs"
    - "combinatorial optimization with graph convolutional networks and guided tree search"

Connected through hyperedge HE-3 (induced by source paper "extending tensorflow s semantics with pipelined execution"):
  with members:
    - "in datacenter performance analysis of a tensor processing unit"
    - "execution templates caching control plane decisions for strong scaling of data analytics"
    - "a survey on deep learning in medical image analysis"
    - "big data spark solution for functional magnetic resonance imaging"
    - "improved techniques for training gans"
    - "scalable fast cloud computing with execution templates"
    - "faster and more accurate sequence alignment with snap"
    - "petascale computational systems"

Connected through hyperedge HE-4 (induced by source paper "the design and implementation of a scalable dl benchmarking platform"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "aibench an industry standard internet service ai benchmark suite"
    - "analysis of dawnbench a time to accuracy machine learning performance benchmark"
    - "tbd benchmarking and analyzing deep neural network training"
    - "tensorflow serving flexible high performance ml serving"
    - "clipper a low latency online prediction serving system"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"

Connected through hyperedge HE-5 (induced by source paper "auto vectorizing tensorflow graphs jacobians auto batching and beyond"):
  with members:
    - "spectral inference networks unifying spectral methods with deep learning"
    - "deep learning with dynamic computation graphs"
    - "dlvm a modern compiler infrastructure for deep learning systems"
    - "neural message passing for quantum chemistry"
    - "adaptive computation time for recurrent neural networks"
    - "improved semantic representations from tree structured long short term memory networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "no dnn left behind improving inference in the cloud with multi tenancy"):
  with members:
    - "pretzel opening the black box of machine learning prediction serving systems"
    - "improving the expressiveness of deep learning frameworks with recursion"
    - "serving deep learning models in a serverless platform"
    - "clipper a low latency online prediction serving system"
    - "tensorflow a system for large scale machine learning"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "janus fast and flexible deep learning via symbolic graph execution of imperative programs"):
  with members:
    - "autograph imperative style coding with graph based performance"
    - "improving the expressiveness of deep learning frameworks with recursion"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "deep learning with dynamic computation graphs"
    - "device placement optimization with reinforcement learning"
    - "proximal policy optimization algorithms"
    - "asynchronous methods for deep reinforcement learning"
    - "image to image translation with conditional adversarial 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.DC(Distributed, Parallel, and Cluster Computing)