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: "tensorflow serving flexible high performance ml serving"
  Abstract: We describe TensorFlow-Serving, a system to serve machine learning models inside Google which is also available in the cloud and via open-source. It is extremely flexible in terms of the types of ML platforms it supports, and ways to integrate with systems that convey new models and updated versions from training to serving. At the same time, the core code paths around model lookup and inference have been carefully optimized to avoid performance pitfalls observed in naive implementations. Google uses it in many production deployments, including a multi-tenant model hosting service called TFS^2.

Connected through hyperedge HE-1 (induced by source paper "aibench an agile domain specific benchmarking methodology and an ai benchmark suite"):
  with members:
    - "mlperf training benchmark"
    - "ranking distillation learning compact ranking models with high performance for recommender system"
    - "frustrated with replicating claims of a shared model a solution"
    - "bigdatabench a scalable and unified big data and ai benchmark suite"
    - "data motifs a lens towards fully understanding big data and ai workloads"
    - "tbd benchmarking and analyzing deep neural network training"
    - "in datacenter performance analysis of a tensor processing unit"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-2 (induced by source paper "flexserve deployment of pytorch models as flexible rest endpoints"):
  with members:
    - "losing confidence in quality unspoken evolution of computer vision services"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "aibench an industry standard internet service ai benchmark suite"):
  with members:
    - "edge aibench towards comprehensive end to end edge computing benchmarking"
    - "ranking distillation learning compact ranking models with high performance for recommender system"
    - "frustrated with replicating claims of a shared model a solution"
    - "bigdatabench a scalable and unified big data and ai benchmark suite"
    - "data motifs a lens towards fully understanding big data and ai workloads"
    - "tbd benchmarking and analyzing deep neural network training"
    - "in datacenter performance analysis of a tensor processing unit"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-4 (induced by source paper "characterizing the deep neural networks inference performance of mobile applications"):
  with members:
    - "no dnn left behind improving inference in the cloud with multi tenancy"
    - "deepcache principled cache for mobile deep vision"
    - "nestdnn resource aware multi tenant on device deep learning for continuous mobile vision"
    - "a first look at deep learning apps on smartphones"
    - "serving deep learning models in a serverless platform"
    - "learning transferable architectures for scalable image recognition"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-5 (induced by source paper "modelhub ai dissemination platform for deep learning models"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "yolov3 an incremental improvement"
    - "arcface additive angular margin loss for deep face recognition"
    - "dlhub model and data serving for science"
    - "unsupervised odometry and depth learning for endoscopic capsule robots"
    - "identifying the best machine learning algorithms for brain tumor segmentation progression assessment and overall survival prediction in the brats challenge"
    - "nips not even wrong a systematic review of empirically complete demonstrations of algorithmic effectiveness in the machine learning and artificial intelligence literature"
    - "understanding convolution for semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "modipick sla aware accuracy optimization for mobile deep inference"):
  with members:
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "in datacenter performance analysis of a tensor processing unit"
    - "learning transferable architectures for scalable image recognition"
    - "deepasl enabling ubiquitous and non intrusive word and sentence level sign language translation"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "cloud based or on device an empirical study of mobile deep inference"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "clipper a low latency online prediction serving system"

Connected through hyperedge HE-7 (induced by source paper "willump a statistically aware end to end optimizer for machine learning inference"):
  with members:
    - "accelerating deep learning inference via freezing"
    - "gradient boosted feature selection"
    - "parity models a general framework for coding based resilience in ml inference"
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "focus querying large video datasets with low latency and low cost"
    - "idk cascades fast deep learning by learning not to overthink"
    - "noscope optimizing neural network queries over video at scale"
    - "clipper a low latency online prediction serving system"

Connected through hyperedge HE-8 (induced by source paper "perseus characterizing performance and cost of multi tenant serving for cnn models"):
  with members:
    - "barista efficient and scalable serverless serving system for deep learning prediction services"
    - "no dnn left behind improving inference in the cloud with multi tenancy"
    - "dynamic space time scheduling for gpu inference"
    - "mlperf inference benchmark"
    - "infaas a model less inference serving system"
    - "salus fine grained gpu sharing primitives for deep learning applications"
    - "auto tuning neural network quantization framework for collaborative inference between the cloud and edge"
    - "deep learning inference in facebook data centers characterization performance optimizations and hardware implications"

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)