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: "a modular benchmarking infrastructure for high performance and reproducible deep learning"
  Abstract: We introduce Deep500: the first customizable benchmarking infrastructure that enables fair comparison of the plethora of deep learning frameworks, algorithms, libraries, and techniques. The key idea behind Deep500 is its modular design, where deep learning is factorized into four distinct levels: operators, network processing, training, and distributed training. Our evaluation illustrates that Deep500 is customizable (enables combining and benchmarking different deep learning codes) and fair (uses carefully selected metrics). Moreover, Deep500 is fast (incurs negligible overheads), verifiable (offers infrastructure to analyze correctness), and reproducible. Finally, as the first distributed and reproducible benchmarking system for deep learning, Deep500 provides software infrastructure to utilize the most powerful supercomputers for extreme-scale workloads.

Connected through hyperedge HE-1 (induced by source paper "practice of streaming and dynamic graphs concepts models systems and parallelism"):
  with members:
    - "storing and querying large scale spatio temporal graphs with high throughput edge insertions"
    - "parallel batch dynamic graph connectivity"
    - "demystifying graph databases analysis and taxonomy of data organization system designs and graph queries"
    - "graph processing on fpgas taxonomy survey challenges"
    - "slim fly a cost effective low diameter network topology"
    - "red blue pebbling revisited near optimal parallel matrix matrix multiplication"
    - "dynamic algorithms for the massively parallel computation model"
    - "fatpaths routing in supercomputers data centers and clouds with low diameter networks when shortest paths fall short"

Connected through hyperedge HE-2 (induced by source paper "slim graph practical lossy graph compression for approximate graph processing storage and analytics"):
  with members:
    - "demystifying graph databases analysis and taxonomy of data organization system designs and graph queries"
    - "graph processing on fpgas taxonomy survey challenges"
    - "fatpaths routing in supercomputers data centers and clouds with low diameter networks when shortest paths fall short"
    - "active access a mechanism for high performance distributed data centric computations"
    - "communication efficient jaccard similarity for high performance distributed genome comparisons"
    - "local computation algorithms for spanners"
    - "survey and taxonomy of lossless graph compression and space efficient graph representations"
    - "scalable approximation algorithm for graph summarization"

Connected through hyperedge HE-3 (induced by source paper "mlperf training benchmark"):
  with members:
    - "deep learning recommendation model for personalization and recommendation systems"
    - "scalable realistic recommendation datasets through fractal expansions"
    - "optimizing network performance for distributed dnn training on gpu clusters imagenet alexnet training in 1 5 minutes"
    - "scalable methods for 8 bit training of neural networks"
    - "massively distributed sgd imagenet resnet 50 training in a flash"
    - "analysis of dawnbench a time to accuracy machine learning performance benchmark"
    - "nvidia tensor core programmability performance precision"
    - "image classification at supercomputer scale"

Connected through hyperedge HE-4 (induced by source paper "benanza automatic mu benchmark generation to compute lower bound latency and inform optimizations of deep learning models on gpus"):
  with members:
    - "a survey on compiler autotuning using machine learning"
    - "miopen an open source library for deep learning primitives"
    - "xsp across stack profiling and analysis of machine learning models on gpus"
    - "neurovectorizer end to end vectorization with deep reinforcement learning"
    - "analysis of dawnbench a time to accuracy machine learning performance benchmark"
    - "arcface additive angular margin loss for deep face recognition"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-5 (induced by source paper "taming unbalanced training workloads in deep learning with partial collective operations"):
  with members:
    - "priority based parameter propagation for distributed dnn training"
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "sparcml high performance sparse communication for machine learning"
    - "gossipgrad scalable deep learning using gossip communication based asynchronous gradient descent"
    - "stochastic gradient push for distributed deep learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "imagenet training in minutes"

Connected through hyperedge HE-6 (induced by source paper "hpc ai500 a benchmark suite for hpc ai systems"):
  with members:
    - "edge aibench towards comprehensive end to end edge computing benchmarking"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "tbd benchmarking and analyzing deep neural network training"
    - "globenet convolutional neural networks for typhoon eye tracking from remote sensing imagery"
    - "scaling grpc tensorflow on 512 nodes of cori supercomputer"
    - "benchmarking state of the art deep learning software tools"
    - "application of deep convolutional neural networks for detecting extreme weather in climate datasets"

Connected through hyperedge HE-7 (induced by source paper "slim fly a cost effective low diameter network topology"):
  with members:
    - "demystifying graph databases analysis and taxonomy of data organization system designs and graph queries"
    - "graph processing on fpgas taxonomy survey challenges"
    - "red blue pebbling revisited near optimal parallel matrix matrix multiplication"
    - "network accelerated non contiguous memory transfers"
    - "communication efficient jaccard similarity for high performance distributed genome comparisons"
    - "slim graph practical lossy graph compression for approximate graph processing storage and analytics"
    - "survey and taxonomy of lossless graph compression and space efficient graph representations"
    - "scaling betweenness centrality using communication efficient sparse matrix multiplication"

Connected through hyperedge HE-8 (induced by source paper "graph processing on fpgas taxonomy survey challenges"):
  with members:
    - "slim fly a cost effective low diameter network topology"
    - "survey and taxonomy of lossless graph compression and space efficient graph representations"
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "high level programming abstractions for distributed graph processing"
    - "an efficient dispatcher for large scale graphprocessing on opencl based fpgas"
    - "an efficient graph accelerator with parallel data conflict management"
    - "mathematical foundations of the graphblas"
    - "nxgraph an efficient graph processing system on a single machine"

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)