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: "collage inference tolerating stragglers in distributed neural network inference using coding"
  Abstract: MLaaS (ML-as-a-Service) offerings by cloud computing platforms are becoming increasingly popular these days. Pre-trained machine learning models are deployed on the cloud to support prediction based applications and services. For achieving higher throughput, incoming requests are served by running multiple replicas of the model on different machines concurrently. Incidence of straggler nodes in distributed inference is a significant concern since it can increase inference latency, violate SLOs of the service. In this paper, we propose a novel coded inference model to deal with stragglers in distributed image classification. We propose modified single shot object detection models, Collage-CNN models, to provide necessary resilience efficiently. A Collage-CNN model takes collage images formed by combining multiple images as its input and performs multi-image classification in one shot. We generate custom training collages using images from standard image classification datasets and train the model to achieve high classification accuracy. Deploying the Collage-CNN models in the cloud, we demonstrate that the 99th percentile latency can be reduced by 1.45X to 2.46X compared to replication based approaches and without compromising prediction accuracy.

Connected through hyperedge HE-1 (induced by source paper "a locality based approach for coded computation"):
  with members:
    - "entangled polynomial codes for secure private and batch distributed matrix multiplication breaking the cubic barrier"
    - "slack squeeze coded computing for adaptive straggler mitigation"
    - "timely throughput optimal coded computing over cloud networks"
    - "train where the data is a case for bandwidth efficient coded training"
    - "erasurehead distributed gradient descent without delays using approximate gradient coding"
    - "cross subspace alignment codes for coded distributed batch computation"
    - "hierarchical coded computation"
    - "learning a code machine learning for approximate non linear coded computation"

Connected through hyperedge HE-2 (induced by source paper "parity models a general framework for coding based resilience in ml inference"):
  with members:
    - "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"
    - "speech commands a dataset for limited vocabulary speech recognition"

Connected through hyperedge HE-3 (induced by source paper "slack squeeze coded computing for adaptive straggler mitigation"):
  with members:
    - "codedprivateml a fast and privacy preserving framework for distributed machine learning"
    - "parity models a general framework for coding based resilience in ml inference"
    - "hop heterogeneity aware decentralized training"
    - "speeding up distributed machine learning using codes"
    - "oversketch approximate matrix multiplication for the cloud"
    - "lagrange coded computing optimal design for resiliency security and privacy"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"
    - "coding method for parallel iterative linear solver"

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.CV(Computer Vision and Pattern Recognition)