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: "when do redundant requests reduce latency"
  Abstract: Several systems possess the flexibility to serve requests in more than one way. For instance, a distributed storage system storing multiple replicas of the data can serve a request from any of the multiple servers that store the requested data, or a computational task may be performed in a compute-cluster by any one of multiple processors. In such systems, the latency of serving the requests may potentially be reduced by sending "redundant requests": a request may be sent to more servers than needed, and it is deemed served when the requisite number of servers complete service. Such a mechanism trades off the possibility of faster execution of at least one copy of the request with the increase in the delay due to an increased load on the system. Due to this tradeoff, it is unclear when redundant requests may actually help. Several recent works empirically evaluate the latency performance of redundant requests in diverse settings. #R##N#This work aims at an analytical study of the latency performance of redundant requests, with the primary goals of characterizing under what scenarios sending redundant requests will help (and under what scenarios they will not help), as well as designing optimal redundant-requesting policies. We first present a model that captures the key features of such systems. We show that when service times are i.i.d. memoryless or "heavier", and when the additional copies of already-completed jobs can be removed instantly, redundant requests reduce the average latency. On the other hand, when service times are "lighter" or when service times are memoryless and removal of jobs is not instantaneous, then not having any redundancy in the requests is optimal under high loads. Our results hold for arbitrary arrival processes.

Connected through hyperedge HE-1 (induced by source paper "straggler resistant distributed matrix computation via coding theory"):
  with members:
    - "numerically stable polynomially coded computing"
    - "distributed matrix vector multiplication a convolutional coding approach"
    - "random khatri rao product codes for numerically stable distributed matrix multiplication"
    - "random convolutional coding for robust and straggler resilient distributed matrix computation"
    - "numerically stable coded matrix computations via circulant and rotation matrix embeddings"
    - "universally decodable matrices for distributed matrix vector multiplication"
    - "speeding up distributed machine learning using codes"
    - "straggler mitigation in distributed matrix multiplication fundamental limits and optimal coding"

Connected through hyperedge HE-2 (induced by source paper "improving the performance of heterogeneous data centers through redundancy"):
  with members:
    - "balanced fair resource sharing in computer clusters"

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 "collage inference tolerating stragglers in distributed neural network inference using coding"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "yolov3 an incremental improvement"
    - "coded computation over heterogeneous clusters"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "dssd deconvolutional single shot detector"
    - "short dot computing large linear transforms distributedly using coded short dot products"
    - "dsod learning deeply supervised object detectors from scratch"

Connected through hyperedge HE-5 (induced by source paper "analyzing the download time of availability codes"):
  with members:
    - "straggler mitigation at scale"
    - "simplex queues for hot data download"
    - "on the service capacity region of accessing erasure coded content"
    - "rate optimal binary linear locally repairable codes with small availability"
    - "efficient redundancy techniques for latency reduction in cloud systems"
    - "provably delay efficient data retrieving in storage clouds"
    - "repair locality with multiple erasure tolerance"
    - "locality and availability in distributed storage"

Connected through hyperedge HE-6 (induced by source paper "collage inference using coded redundancy for low variance distributed image classification"):
  with members:
    - "parity models a general framework for coding based resilience in ml inference"
    - "learning a code machine learning for approximate non linear coded computation"
    - "speeding up distributed machine learning using codes"
    - "yolov3 an incremental improvement"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "dsod learning deeply supervised object detectors from scratch"
    - "a fundamental tradeoff between computation and communication in distributed computing"

Connected through hyperedge HE-7 (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"
    - "collage inference tolerating stragglers in distributed neural network inference using coding"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "distributed matrix multiplication using speed adaptive coding"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "oversketch approximate matrix multiplication for the cloud"
    - "lagrange coded computing optimal design for resiliency security and privacy"
    - "coded computation over heterogeneous clusters"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"
    - "coding method for parallel iterative linear solver"
    - "short dot computing large linear transforms distributedly using coded short dot products"
    - "a sequential approximation framework for coded distributed optimization"

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.NI(Networking and Internet Architecture)