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: "deep learning inference in facebook data centers characterization performance optimizations and hardware implications"
  Abstract: The application of deep learning techniques resulted in remarkable improvement of machine learning models. In this paper provides detailed characterizations of deep learning models used in many Facebook social network services. We present computational characteristics of our models, describe high performance optimizations targeting existing systems, point out their limitations and make suggestions for the future general-purpose/accelerated inference hardware. Also, we highlight the need for better co-design of algorithms, numerics and computing platforms to address the challenges of workloads often run in data centers.

Connected through hyperedge HE-1 (induced by source paper "improving efficiency in neural network accelerator using operands hamming distance optimization"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "eyeriss v2 a flexible accelerator for emerging deep neural networks on mobile devices"
    - "mnasnet platform aware neural architecture search for mobile"
    - "in datacenter performance analysis of a tensor processing unit"
    - "hello edge keyword spotting on microcontrollers"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-2 (induced by source paper "the two pass softmax algorithm"):
  with members:
    - "in datacenter performance analysis of a tensor processing unit"
    - "building a web scale dependency parsed corpus from commoncrawl"
    - "high performance ultra low precision convolutions on mobile devices"
    - "efficient softmax approximation for gpus"
    - "eie efficient inference engine on compressed deep neural network"
    - "faster cnns with direct sparse convolutions and guided pruning"
    - "exploring the limits of language modeling"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-3 (induced by source paper "parity models a general framework for coding based resilience in ml inference"):
  with members:
    - "collage inference tolerating stragglers in distributed neural network inference using coding"
    - "pretzel opening the black box of machine learning prediction serving systems"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning a code machine learning for approximate non linear coded computation"
    - "leveraging coding techniques for speeding up distributed computing"
    - "speeding up distributed machine learning using codes"
    - "rafiki machine learning as an analytics service system"
    - "tvm an automated end to end optimizing compiler for deep learning"

Connected through hyperedge HE-4 (induced by source paper "characterizing deep learning training workloads on alibaba pai"):
  with members:
    - "scalable deep learning on distributed infrastructures challenges techniques and tools"
    - "data motif based proxy benchmarks for big data and ai workloads"
    - "graph convolutional neural networks for web scale recommender systems"
    - "communication scheduling as a first class citizen in distributed machine learning systems"
    - "data motifs a lens towards fully understanding big data and ai workloads"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "fusionstitching deep fusion and code generation for tensorflow computations on gpus"
    - "predicting the computational cost of deep learning models"

Connected through hyperedge HE-5 (induced by source paper "mixed dimension embeddings with application to memory efficient recommendation systems"):
  with members:
    - "on the dimensionality of embeddings for sparse features and data"
    - "deep learning recommendation model for personalization and recommendation systems"
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "saec similarity aware embedding compression in recommendation systems"
    - "practice on long sequential user behavior modeling for click through rate prediction"
    - "tensorized embedding layers for efficient model compression"
    - "hierarchy based image embeddings for semantic image retrieval"
    - "block based singular value decomposition approach to matrix factorization for recommender systems"

Connected through hyperedge HE-6 (induced by source paper "spring a sparsity aware reduced precision monolithic 3d cnn accelerator architecture for training and inference"):
  with members:
    - "software defined design space exploration for an efficient ai accelerator architecture"
    - "mixed precision training of convolutional neural networks using integer operations"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "image classification at supercomputer scale"
    - "ucnn exploiting computational reuse in deep neural networks via weight repetition"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "progressive neural architecture search"
    - "in datacenter performance analysis of a tensor processing unit"

Connected through hyperedge HE-7 (induced by source paper "the architectural implications of facebook s dnn based personalized recommendation"):
  with members:
    - "on the dimensionality of embeddings for sparse features and data"
    - "deep learning recommendation model for personalization and recommendation systems"
    - "tensordimm a practical near memory processing architecture for embeddings and tensor operations in deep learning"
    - "mlperf training benchmark"
    - "recnmp accelerating personalized recommendation with near memory processing"
    - "bandana using non volatile memory for storing deep learning models"
    - "tbd benchmarking and analyzing deep neural network training"
    - "imagenet training in minutes"

Connected through hyperedge HE-8 (induced by source paper "prema a predictive multi task scheduling algorithm for preemptible neural processing units"):
  with members:
    - "tensordimm a practical near memory processing architecture for embeddings and tensor operations in deep learning"
    - "beyond the memory wall a case for memory centric hpc system for deep learning"
    - "beyond human level accuracy computational challenges in deep learning"
    - "scale sim systolic cnn accelerator simulator"
    - "in datacenter performance analysis of a tensor processing unit"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "massive exploration of neural machine translation architectures"

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)