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: "a new combinatorial design of coded distributed computing"
  Abstract: Coded distributed computing introduced by Li et al. in 2015 is an efficient approach to trade computing power to reduce the communication load in general distributed computing frameworks such as MapReduce. In particular, Li et al. show that increasing the computation load in the Map phase by a factor of $r$ can create coded multicasting opportunities to reduce the communication load in the Reduce phase by the same factor. However, there are two major limitations in practice. First, it requires an exponentially large number of input files (data batches) when the number of computing nodes gets large. Second, it forces every $s$ computing nodes to compute one Map function, which leads to a large number of Map functions required to achieve the promised gain. In this paper, we make an attempt to overcome these two limitations by proposing a novel coded distributed computing approach based on a combinatorial design. We demonstrate that when the number of computing nodes becomes large, 1) the proposed approach requires an exponentially less number of input files; 2) the required number of Map functions is also reduced exponentially. Meanwhile, the resulting computation-communication trade-off maintains the multiplicative gain compared to conventional uncoded unicast and achieves the information theoretic lower bound asymmetrically for some system parameters.

Connected through hyperedge HE-1 (induced by source paper "coded distributed computing schemes with smaller numbers of input files and output functions"):
  with members:
    - "resolvable designs for speeding up distributed computing"
    - "speeding up distributed machine learning using codes"
    - "storage computation and communication a fundamental tradeoff in distributed computing"
    - "coded computing for distributed graph analytics"
    - "hierarchical coding for distributed computing"
    - "compressed coded distributed computing"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"
    - "on the placement delivery array design for centralized coded caching scheme"

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"
    - "leveraging coding techniques for speeding up distributed computing"
    - "storage computation and communication a fundamental tradeoff in distributed computing"
    - "wireless mapreduce 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:
    - "leveraging coding techniques for speeding up distributed computing"
    - "coded computing for distributed graph analytics"
    - "compressed 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"
    - "leveraging coding techniques for speeding up distributed computing"
    - "distributed computing with heterogeneous communication constraints the worst case computation load and proof by contradiction"
    - "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 "cascaded coded distributed computing on heterogeneous networks"):
  with members:
    - "leveraging coding techniques for speeding up distributed computing"
    - "distributed computing with heterogeneous communication constraints the worst case computation load and proof by contradiction"
    - "on heterogeneous coded distributed computing"
    - "a fundamental tradeoff between computation and communication in distributed computing"

Connected through hyperedge HE-6 (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"
    - "leveraging coding techniques for speeding up distributed computing"
    - "coded distributed computing with node cooperation substantially increases speedup factors"
    - "achieving spatial scalability for coded caching over wireless networks"
    - "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-7 (induced by source paper "wireless mapreduce distributed computing"):
  with members:
    - "leveraging coding techniques for speeding up distributed computing"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "fundamental limits of decentralized data shuffling"):
  with members:
    - "coded distributed computing with heterogeneous function assignments"
    - "cascaded coded distributed computing on heterogeneous networks"
    - "embedded index coding"
    - "the exact rate memory tradeoff for caching with uncoded prefetching"
    - "leveraging coding techniques for speeding up distributed computing"
    - "speeding up distributed machine learning using codes"
    - "storage computation and communication a fundamental tradeoff in distributed computing"
    - "coded computing for distributed graph analytics"

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.DC(Distributed, Parallel, and Cluster Computing)