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: "runtime concurrency control and operation scheduling for high performance neural network training"
  Abstract: Training neural network often uses a machine learning framework such as TensorFlow and Caffe2. These frameworks employ a dataflow model where the NN training is modeled as a directed graph composed of a set of nodes. Operations in neural network training are typically implemented by the frameworks as primitives and represented as nodes in the dataflow graph. Training NN models in a dataflow-based machine learning framework involves a large number of fine-grained operations. Those operations have diverse memory access patterns and computation intensity. How to manage and schedule those operations is challenging, because we have to decide the number of threads to run each operation (concurrency control) and schedule those operations for good hardware utilization and system throughput. #R##N#In this paper, we extend an existing runtime system (the TensorFlow runtime) to enable automatic concurrency control and scheduling of operations. We explore performance modeling to predict the performance of operations with various thread-level parallelism. Our performance model is highly accurate and lightweight. Leveraging the performance model, our runtime system employs a set of scheduling strategies that co-run operations to improve hardware utilization and system throughput. Our runtime system demonstrates a big performance benefit. Comparing with using the recommended configurations for concurrency control and operation scheduling in TensorFlow, our approach achieves 33% performance (execution time) improvement on average (up to 49%) for three neural network models, and achieves high performance closing to the optimal one manually obtained by the user.

Connected through hyperedge HE-1 (induced by source paper "flame a self adaptive auto labeling system for heterogeneous mobile processors"):
  with members:
    - "cross modal data programming enables rapid medical machine learning"
    - "federated learning strategies for improving communication efficiency"
    - "deep co training for semi supervised image recognition"
    - "deeprebirth accelerating deep neural network execution on mobile devices"
    - "inferring generative model structure with static analysis"
    - "snorkel rapid training data creation with weak supervision"
    - "morphnet fast simple resource constrained structure learning of deep networks"
    - "learning the structure of generative models without labeled data"

Connected through hyperedge HE-2 (induced by source paper "performance analysis and characterization of training deep learning models on mobile devices"):
  with members:
    - "towards federated learning at scale system design"
    - "agnostic federated learning"
    - "applied federated learning improving google keyboard query suggestions"
    - "deepcache principled cache for mobile deep vision"
    - "federated learning strategies for improving communication efficiency"
    - "beyond data and model parallelism for deep neural networks"
    - "latency and throughput characterization of convolutional neural networks for mobile computer vision"
    - "adaptive federated learning in resource constrained edge computing systems"

Connected through hyperedge HE-3 (induced by source paper "performance analysis and characterization of training deep learning models on nvidia tx2"):
  with members:
    - "towards federated learning at scale system design"
    - "agnostic federated learning"
    - "applied federated learning improving google keyboard query suggestions"
    - "deepcache principled cache for mobile deep vision"
    - "federated learning strategies for improving communication efficiency"
    - "beyond data and model parallelism for deep neural networks"
    - "federated optimization for heterogeneous networks"
    - "adaptive federated learning in resource constrained edge computing systems"

Connected through hyperedge HE-4 (induced by source paper "sentinel runtime data management on heterogeneous main memorysystems for deep learning"):
  with members:
    - "multi tier buffer management and storage system design for non volatile memory"
    - "basic performance measurements of the intel optane dc persistent memory module"
    - "pretzel opening the black box of machine learning prediction serving systems"
    - "exascale deep learning for climate analytics"
    - "superneurons dynamic gpu memory management for training deep neural networks"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"
    - "optimizing cnn model inference on cpus"
    - "ray a distributed framework for emerging ai applications"

Connected through hyperedge HE-5 (induced by source paper "scheduling optimization of parallel linear algebra algorithms using supervised learning"):
  with members:
    - "hpx smart executors"
    - "expression templates revisited a performance analysis of current methodologies"

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)