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: "mlperf inference benchmark"
  Abstract: Machine-learning (ML) hardware and software system demand is burgeoning. Driven by ML applications, the number of different ML inference systems has exploded. Over 100 organizations are building ML inference chips, and the systems that incorporate existing models span at least three orders of magnitude in power consumption and four orders of magnitude in performance; they range from embedded devices to data-center solutions. Fueling the hardware are a dozen or more software frameworks and libraries. The myriad combinations of ML hardware and ML software make assessing ML-system performance in an architecture-neutral, representative, and reproducible manner challenging. There is a clear need for industry-wide standard ML benchmarking and evaluation criteria. MLPerf Inference answers that call. Driven by more than 30 organizations as well as more than 200 ML engineers and practitioners, MLPerf implements a set of rules and practices to ensure comparability across systems with wildly differing architectures. In this paper, we present the method and design principles of the initial MLPerf Inference release. The first call for submissions garnered more than 600 inference-performance measurements from 14 organizations, representing over 30 systems that show a range of capabilities.

Connected through hyperedge HE-1 (induced by source paper "deeprecsys a system for optimizing end to end at scale neural recommendation inference"):
  with members:
    - "the architectural implications of facebook s dnn based personalized recommendation"
    - "deep learning recommendation model for personalization and recommendation systems"
    - "mixed dimension embeddings with application to memory efficient recommendation systems"
    - "prema a predictive multi task scheduling algorithm for preemptible neural processing units"
    - "tensordimm a practical near memory processing architecture for embeddings and tensor operations in deep learning"
    - "compositional embeddings using complementary partitions for memory efficient recommendation systems"
    - "mlperf training benchmark"
    - "benchmarking tpu gpu and cpu platforms for deep learning"

Connected through hyperedge HE-2 (induced by source paper "mnn a universal and efficient inference engine"):
  with members:
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "dlvm a modern compiler infrastructure for deep learning systems"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "tensorflow a system for large scale machine learning"
    - "deep residual learning for image recognition"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "fast algorithms for convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "benchmarking tinyml systems challenges and direction"):
  with members:
    - "pulp nn accelerating quantized neural networks on parallel ultra low power risc v processors"
    - "visual wake words dataset"
    - "sparse sparse architecture search for cnns on resource constrained microcontrollers"
    - "haq hardware aware automated quantization with mixed precision"
    - "cmsis nn efficient neural network kernels for arm cortex m cpus"
    - "hello edge keyword spotting on microcontrollers"
    - "recognizing detailed human context in the wild from smartphones and smartwatches"

Connected through hyperedge HE-4 (induced by source paper "dlspec a deep learning task exchange specification"):
  with members:
    - "codereef an open platform for portable mlops reusable automation actions and reproducible benchmarking"
    - "the design and implementation of a scalable dl benchmarking platform"
    - "mlperf training benchmark"

Connected through hyperedge HE-5 (induced by source paper "developing a recommendation benchmark for mlperf training and inference"):
  with members:
    - "deep learning recommendation model for personalization and recommendation systems"
    - "scalable realistic recommendation datasets through fractal expansions"
    - "deep cross network for ad click predictions"

Connected through hyperedge HE-6 (induced by source paper "smaug end to end full stack simulation infrastructure for deep learning workloads"):
  with members:
    - "optimizing gpu cache policies for mi workloads"
    - "asv accelerated stereo vision system"
    - "exploiting parallelism opportunities with deep learning frameworks"
    - "euphrates algorithm soc co design for low power mobile continuous vision"
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "deep learning inference in facebook data centers characterization performance optimizations and hardware implications"
    - "eva 2 exploiting temporal redundancy in live computer vision"
    - "in datacenter performance analysis of a tensor processing unit"

Connected through hyperedge HE-7 (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"
    - "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"
    - "optimizing cnn model inference on cpus"

Connected through hyperedge HE-8 (induced by source paper "benchcouncil s view on benchmarking ai and other emerging workloads"):
  with members:
    - "aibench an industry standard internet service ai benchmark suite"
    - "edge aibench towards comprehensive end to end edge computing benchmarking"
    - "mlperf training benchmark"
    - "data motif based proxy benchmarks for big data and ai workloads"
    - "benchmark analysis of representative deep neural network architectures"
    - "bigdatabench a scalable and unified big data and ai benchmark suite"
    - "data motifs a lens towards fully understanding big data and ai workloads"

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.LG(Machine Learning)