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: "near optimal coded data shuffling for distributed learning"
  Abstract: Data shuffling between distributed cluster of nodes is one of the critical steps in implementing large-scale learning algorithms. Randomly shuffling the data-set among a cluster of workers allows different nodes to obtain fresh data assignments at each learning epoch. This process has been shown to provide improvements in the learning process. However, the statistical benefits of distributed data shuffling come at the cost of extra communication overhead from the master node to worker nodes, and can act as one of the major bottlenecks in the overall time for computation. There has been significant recent interest in devising approaches to minimize this communication overhead. One approach is to provision for extra storage at the computing nodes. The other emerging approach is to leverage coded communication to minimize the overall communication overhead. #R##N#The focus of this work is to understand the fundamental trade-off between the amount of storage and the communication overhead for distributed data shuffling. In this work, we first present an information theoretic formulation for the data shuffling problem, accounting for the underlying problem parameters (number of workers, $K$, number of data points, $N$, and the available storage, $S$ per node). We then present an information theoretic lower bound on the communication overhead for data shuffling as a function of these parameters. We next present a novel coded communication scheme and show that the resulting communication overhead of the proposed scheme is within a multiplicative factor of at most $\frac{K}{K-1}$ from the information-theoretic lower bound. Furthermore, we present the aligned coded shuffling scheme for some storage values, which achieves the optimal storage vs communication trade-off for $K<5$, and further reduces the maximum multiplicative gap down to $\frac{K-\frac{1}{3}}{K-1}$, for $K\geq 5$.

Connected through hyperedge HE-1 (induced by source paper "on optimal load memory tradeoff of cache aided scalar linear function retrieval"):
  with members:
    - "codedprivateml a fast and privacy preserving framework for distributed machine learning"
    - "on coded caching with private demands"
    - "device to device private caching with trusted server"
    - "private and secure distributed matrix multiplication with flexible communication load"
    - "on the optimality of d2d coded caching with uncoded cache placement and one shot delivery"
    - "the exact rate memory tradeoff for caching with uncoded prefetching"
    - "on the fundamental limits of coded data shuffling for distributed learning systems"
    - "fundamental limits of decentralized data shuffling"

Connected through hyperedge HE-2 (induced by source paper "heterogeneous computation assignments in coded elastic computing"):
  with members:
    - "an optimal iterative placement algorithm for pir from heterogeneous storage constrained databases"
    - "optimizing the transition waste in coded elastic computing"
    - "speeding up distributed machine learning using codes"
    - "coded elastic computing"
    - "straggler mitigation in distributed matrix multiplication fundamental limits and optimal coding"
    - "a fundamental tradeoff between computation and communication in distributed computing"

Connected through hyperedge HE-3 (induced by source paper "the capacity of private information retrieval from uncoded storage constrained databases"):
  with members:
    - "robust private information retrieval from coded systems with byzantine and colluding servers"
    - "private information retrieval through wiretap channel ii privacy meets security"
    - "asymmetry hurts private information retrieval under asymmetric traffic constraints"
    - "multi file private information retrieval from mds coded databases with colluding servers"
    - "fundamental limits of cache aided private information retrieval with unknown and uncoded prefetching"
    - "the capacity of private information retrieval with private side information"
    - "the capacity of cache aided private information retrieval"
    - "the capacity of private information retrieval from byzantine and colluding databases"

Connected through hyperedge HE-4 (induced by source paper "coded computing for distributed graph analytics"):
  with members:
    - "coded cooperative computation for internet of things"
    - "leveraging coding techniques for speeding up distributed computing"
    - "speeding up distributed machine learning using codes"
    - "distributed computing with heterogeneous communication constraints the worst case computation load and proof by contradiction"
    - "compressed coded distributed computing"
    - "fundamental limits of coded linear transform"
    - "fundamental resource trade offs for encoded distributed optimization"
    - "rateless codes for near perfect load balancing in distributed matrix vector multiplication"

Connected through hyperedge HE-5 (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"

Connected through hyperedge HE-6 (induced by source paper "coded iterative computing using substitute decoding"):
  with members:
    - "fundamental limits of coded linear transform"
    - "on the optimal recovery threshold of coded matrix multiplication"
    - "rateless codes for near perfect load balancing in distributed matrix vector multiplication"
    - "coded sparse matrix multiplication"
    - "straggler mitigation by delayed relaunch of tasks"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"
    - "short dot computing large linear transforms distributedly using coded short dot products"
    - "gradient coding from cyclic mds codes and expander graphs"

Connected through hyperedge HE-7 (induced by source paper "on the fundamental limits of coded data shuffling for distributed learning systems"):
  with members:
    - "the exact rate memory tradeoff for caching with uncoded prefetching"
    - "speeding up distributed machine learning using codes"
    - "a pliable index coding approach to data shuffling"
    - "a fundamental tradeoff between computation and communication in distributed computing"
    - "a unified coding framework for distributed computing with straggling servers"
    - "on the worst case communication overhead for distributed data shuffling"
    - "fundamental limits of caching"

Connected through hyperedge HE-8 (induced by source paper "a pliable index coding approach to data shuffling"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "a fundamental tradeoff between computation and communication in distributed computing"
    - "a unified coding framework for distributed computing with straggling servers"
    - "information theoretic limits of data shuffling for distributed learning"
    - "the minrank of random graphs"
    - "a polynomial time algorithm for pliable index coding"
    - "content type coding"
    - "fundamental limits of caching"

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)