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: "how to optimally allocate resources for coded distributed computing"
  Abstract: Today's data centers have an abundance of computing resources, hosting server clusters consisting of as many as tens or hundreds of thousands of machines. To execute a complex computing task over a data center, it is natural to distribute computations across many nodes to take advantage of parallel processing. However, as we allocate more and more computing resources to a computation task and further distribute the computations, large amounts of (partially) computed data must be moved between consecutive stages of computation tasks among the nodes, hence the communication load can become the bottleneck. In this paper, we study the optimal allocation of computing resources in distributed computing, in order to minimize the total execution time in distributed computing accounting for both the duration of computation and communication phases. In particular, we consider a general MapReduce-type distributed computing framework, in which the computation is decomposed into three stages: \emph{Map}, \emph{Shuffle}, and \emph{Reduce}. We focus on a recently proposed \emph{Coded Distributed Computing} approach for MapReduce and study the optimal allocation of computing resources in this framework. For all values of problem parameters, we characterize the optimal number of servers that should be used for distributed processing, provide the optimal placements of the Map and Reduce tasks, and propose an optimal coded data shuffling scheme, in order to minimize the total execution time. To prove the optimality of the proposed scheme, we first derive a matching information-theoretic converse on the execution time, then we prove that among all possible resource allocation schemes that achieve the minimum execution time, our proposed scheme uses the exactly minimum possible number of servers.

Connected through hyperedge HE-1 (induced by source paper "on the optimality of d2d coded caching with uncoded cache placement and one shot delivery"):
  with members:
    - "the exact rate memory tradeoff for caching with uncoded prefetching"
    - "speeding up distributed machine learning using codes"
    - "wireless mapreduce distributed computing"
    - "a pliable index coding approach to data shuffling"
    - "coded computation over heterogeneous clusters"
    - "characterizing the rate memory tradeoff in cache networks within a factor of 2"
    - "coded caching in partially cooperative d2d communication networks"
    - "minimizing latency for secure distributed computing"

Connected through hyperedge HE-2 (induced by source paper "a fundamental storage communication tradeoff in distributed computing with straggling nodes"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "storage computation and communication a fundamental tradeoff in distributed computing"
    - "speeding up distributed gradient descent by utilizing non persistent stragglers"
    - "hierarchical coding for distributed computing"
    - "placement delivery array design through strong edge coloring of bipartite graphs"
    - "a storage computation communication tradeoff for distributed computing"
    - "coded distributed computing with node cooperation substantially increases speedup factors"
    - "straggler mitigation in distributed matrix multiplication fundamental limits and optimal coding"

Connected through hyperedge HE-3 (induced by source paper "the exact rate memory tradeoff for caching with uncoded prefetching"):
  with members:
    - "coding for distributed fog computing"
    - "characterizing the rate memory tradeoff in cache networks within a factor of 2"
    - "information theoretic caching the multi user case"
    - "degrees of freedom of cache aided wireless interference networks"
    - "noisy broadcast networks with receiver caching"
    - "a fundamental tradeoff between computation and communication in distributed computing"
    - "fundamental limits of cache aided interference management"
    - "coded caching for a large number of users"

Connected through hyperedge HE-4 (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-5 (induced by source paper "near optimal coded data shuffling for distributed learning"):
  with members:
    - "the exact rate memory tradeoff for caching with uncoded prefetching"
    - "speeding up distributed machine learning using codes"
    - "on the fundamental limits of coded data shuffling for distributed learning systems"
    - "a pliable index coding approach to data shuffling"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"
    - "low subpacketization schemes for coded caching"
    - "latency analysis of coded computation schemes over wireless networks"
    - "communication vs distributed computation an alternative trade off curve"

Connected through hyperedge HE-6 (induced by source paper "lagrange coded computing optimal design for resiliency security and privacy"):
  with members:
    - "codedprivateml a fast and privacy preserving framework for distributed machine learning"
    - "polyshard coded sharding achieves linearly scaling efficiency and security simultaneously"
    - "leveraging coding techniques for speeding up distributed computing"
    - "speeding up distributed machine learning using codes"
    - "coded computing for distributed graph analytics"
    - "fundamental limits of coded linear transform"
    - "straggler mitigation in distributed matrix multiplication fundamental limits and optimal coding"
    - "minimizing latency for secure coded computing using secret sharing via staircase codes"

Connected through hyperedge HE-7 (induced by source paper "storage computation and communication a fundamental tradeoff in distributed computing"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "hierarchical coding for distributed computing"
    - "placement delivery array design through strong edge coloring of bipartite graphs"
    - "a storage computation communication tradeoff for distributed computing"
    - "coded distributed computing with node cooperation substantially increases speedup factors"
    - "straggler mitigation in distributed matrix multiplication fundamental limits and optimal coding"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"
    - "communication vs distributed computation an alternative trade off curve"

Connected through hyperedge HE-8 (induced by source paper "distributed computing with heterogeneous communication constraints the worst case computation load and proof by contradiction"):
  with members:
    - "latency analysis of coded computation schemes over wireless networks"
    - "coded convolution for parallel and distributed computing within a deadline"
    - "short dot computing large linear transforms distributedly using coded short dot products"
    - "on heterogeneous coded distributed computing"
    - "on the worst case communication overhead for distributed data shuffling"

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)