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: "bigdl a distributed deep learning framework for big data"
  Abstract: In this paper, we present BigDL, a distributed deep learning framework for Big Data platforms and workflows. It is implemented on top of Apache Spark, and allows users to write their deep learning applications as standard Spark programs (running directly on large-scale big data clusters in a distributed fashion). It provides an expressive, "data-analytics integrated" deep learning programming model, so that users can easily build the end-to-end analytics + AI pipelines under a unified programming paradigm; by implementing an AllReduce like operation using existing primitives in Spark (e.g., shuffle, broadcast, and in-memory data persistence), it also provides a highly efficient "parameter server" style architecture, so as to achieve highly scalable, data-parallel distributed training. Since its initial open source release, BigDL users have built many analytics and deep learning applications (e.g., object detection, sequence-to-sequence generation, visual similarity, neural recommendations, fraud detection, etc.) on Spark.

Connected through hyperedge HE-1 (induced by source paper "cluster pruning an efficient filter pruning method for edge ai vision applications"):
  with members:
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "tiny dsod lightweight object detection for resource restricted usages"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "network decoupling from regular to depthwise separable convolutions"
    - "deep neural network compression with single and multiple level quantization"
    - "rethinking the value of network pruning"

Connected through hyperedge HE-2 (induced by source paper "benchmarking contemporary deep learning hardware and frameworks a survey of qualitative metrics"):
  with members:
    - "analysis of dawnbench a time to accuracy machine learning performance benchmark"
    - "improving data quality through deep learning and statistical models"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "chainermn scalable distributed deep learning framework"
    - "deep learning on fpgas past present and future"
    - "theano a python framework for fast computation of mathematical expressions"
    - "tensorflow a system for large scale machine learning"
    - "blocks and fuel frameworks for deep learning"

Connected through hyperedge HE-3 (induced by source paper "toward automatic comparison of visualization techniques application to graph visualization"):
  with members:
    - "evaluating the readability of force directed graph layouts a deep learning approach"
    - "can the early human visual system compete with deep neural networks"
    - "a study and comparison of human and deep learning recognition performance under visual distortions"
    - "xception deep learning with depthwise separable convolutions"
    - "25 years of cnns can we compare to human abstraction capabilities"
    - "learning to count with deep object features"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-4 (induced by source paper "benchmarking deep learning hardware and frameworks qualitative metrics"):
  with members:
    - "improving data quality through deep learning and statistical models"
    - "chainermn scalable distributed deep learning framework"
    - "deep learning on fpgas past present and future"
    - "theano a python framework for fast computation of mathematical expressions"
    - "blocks and fuel frameworks for deep learning"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "purine a bi graph based deep learning framework"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-5 (induced by source paper "machine learning pipelines with modern big data tools for high energy physics"):
  with members:
    - "accurate large minibatch sgd training imagenet in 1 hour"

Connected through hyperedge HE-6 (induced by source paper "democratizing production scale distributed deep learning"):
  with members:
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "tune a research platform for distributed model selection and training"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "ray a distributed framework for emerging ai applications"
    - "scaling sgd batch size to 32k for imagenet training"
    - "extremely large minibatch sgd training resnet 50 on imagenet in 15 minutes"

Connected through hyperedge HE-7 (induced by source paper "gear training a new way to implement high performance model parallel training"):
  with members:
    - "ampnet asynchronous model parallel training for dynamic neural networks"
    - "stochastic gradient descent on highly parallel architectures"
    - "gate variants of gated recurrent unit gru neural networks"
    - "revisiting distributed synchronous sgd"
    - "wide deep learning for recommender systems"
    - "distributed tensorflow with mpi"
    - "deep residual learning for image recognition"

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)