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: "leveraging coding techniques for speeding up distributed computing"
  Abstract: Large scale clusters leveraging distributed computing frameworks such as MapReduce routinely process data that are on the orders of petabytes or more. The sheer size of the data precludes the processing of the data on a single computer. The philosophy in these methods is to partition the overall job into smaller tasks that are executed on different servers; this is called the map phase. This is followed by a data shuffling phase where appropriate data is exchanged between the servers. The final so-called reduce phase, completes the computation. #R##N#One potential approach, explored in prior work for reducing the overall execution time is to operate on a natural tradeoff between computation and communication. Specifically, the idea is to run redundant copies of map tasks that are placed on judiciously chosen servers. The shuffle phase exploits the location of the nodes and utilizes coded transmission. The main drawback of this approach is that it requires the original job to be split into a number of map tasks that grows exponentially in the system parameters. This is problematic, as we demonstrate that splitting jobs too finely can in fact adversely affect the overall execution time. #R##N#In this work we show that one can simultaneously obtain low communication loads while ensuring that jobs do not need to be split too finely. Our approach uncovers a deep relationship between this problem and a class of combinatorial structures called resolvable designs. Appropriate interpretation of resolvable designs can allow for the development of coded distributed computing schemes where the splitting levels are exponentially lower than prior work. We present experimental results obtained on Amazon EC2 clusters for a widely known distributed algorithm, namely TeraSort. We obtain over 4.69$\times$ improvement in speedup over the baseline approach and more than 2.6$\times$ over current state of the art.

Connected through hyperedge HE-1 (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"
    - "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-2 (induced by source paper "heterogeneous coded distributed computing joint design of file allocation and function assignment"):
  with members:
    - "coded distributed computing with heterogeneous function assignments"
    - "cascaded coded distributed computing on heterogeneous networks"
    - "storage computation and communication a fundamental tradeoff in distributed computing"
    - "wireless mapreduce distributed computing"
    - "a new combinatorial design of coded distributed computing"
    - "on heterogeneous coded distributed computing"
    - "a fundamental tradeoff between computation and communication in distributed computing"
    - "a scalable framework for wireless distributed computing"

Connected through hyperedge HE-3 (induced by source paper "resolvable designs for speeding up distributed computing"):
  with members:
    - "coded computing for distributed graph analytics"
    - "compressed coded distributed computing"
    - "a new combinatorial design of coded distributed computing"
    - "communication vs distributed computation an alternative trade off curve"
    - "coded caching schemes with reduced subpacketization from linear block codes"
    - "the benefit of being flexible in distributed computation"
    - "a fundamental tradeoff between computation and communication in distributed computing"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "coded distributed computing with heterogeneous function assignments"):
  with members:
    - "cascaded coded distributed computing on heterogeneous networks"
    - "distributed computing with heterogeneous communication constraints the worst case computation load and proof by contradiction"
    - "a new combinatorial design of coded distributed computing"
    - "on heterogeneous coded distributed computing"
    - "a fundamental tradeoff between computation and communication in distributed computing"
    - "on caching with more users than files"
    - "on the optimality of uncoded cache placement"

Connected through hyperedge HE-5 (induced by source paper "camr coded aggregated mapreduce"):
  with members:
    - "coded computing for distributed graph analytics"
    - "compressed coded distributed computing"
    - "coded caching schemes with reduced subpacketization from linear block codes"
    - "a fundamental tradeoff between computation and communication in distributed computing"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "cascaded coded distributed computing on heterogeneous networks"):
  with members:
    - "distributed computing with heterogeneous communication constraints the worst case computation load and proof by contradiction"
    - "a new combinatorial design of coded distributed computing"
    - "on heterogeneous coded distributed computing"
    - "a fundamental tradeoff between computation and communication in distributed computing"

Connected through hyperedge HE-7 (induced by source paper "fundamental limits of caching in heterogeneous networks with uncoded prefetching"):
  with members:
    - "cooperative multi sender index coding"
    - "the exact rate memory tradeoff for caching with uncoded prefetching"
    - "coded distributed computing with node cooperation substantially increases speedup factors"
    - "achieving spatial scalability for coded caching over wireless networks"
    - "a new combinatorial design of coded distributed computing"
    - "low subpacketization schemes for coded caching"
    - "on the placement delivery array design for centralized coded caching scheme"
    - "coded caching with linear subpacketization is possible using ruzsa szem eredi graphs"

Connected through hyperedge HE-8 (induced by source paper "wireless mapreduce distributed computing"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "federated learning strategies for improving communication efficiency"
    - "distributed computing with heterogeneous communication constraints the worst case computation load and proof by contradiction"
    - "coded computing for distributed graph analytics"
    - "hierarchical coding for distributed computing"
    - "compressed coded distributed computing"
    - "coded distributed computing with node cooperation substantially increases speedup factors"
    - "a new combinatorial design of coded distributed computing"

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.IT(Information Theory)