human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "bivariate polynomial coding for exploiting stragglers in heterogeneous coded computing systems"):
  Abstract: Polynomial coding has been proposed as a solution to the straggler mitigation problem in distributed matrix multiplication. Previous works in the literature employ univariate polynomials to encode matrix partitions. Such schemes greatly improve the speed of distributed computing systems by making the task completion time to depend only on the fastest workers. However, the work done by the slowest workers, which fails to finish the task assigned to them, is completely ignored. In order to exploit the partial computations of the slower workers, we further decompose the overall matrix multiplication task into even smaller subtasks to better fit workers' storage and computation capacities. In this work, we show that univariate schemes fail to make an efficient use of the storage capacity and we propose bivariate polynomial codes. We show that bivariate polynomial codes are a more natural choice to accommodate the additional decomposition of subtasks, as well as, heterogeneous storage and computation resources at workers. However, in contrast to univariate polynomial decoding, for multivariate interpolation guarantying decodability is much harder. We propose two bivartiate polynomial schemes. The first scheme exploits the fact that bivariate interpolation is always possible for rectangular grid of points. We obtain the rectangular grid of points at the cost of allowing some redundant computations. For the second scheme, we relax the decoding constraint, and require decodability for almost all choices of evaluation points. We present interpolation sets satisfying the almost decodability conditions for certain storage configurations of workers. Our numerical results show that bivariate polynomial coding considerably reduces the completion time of distributed matrix multiplication.

Includes members:
  Member "cross subspace alignment codes for coded distributed batch computation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gcsa codes with noise alignment for secure coded multi party batch matrix multiplication")
  Member "hierarchical coded computation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a locality based approach for coded computation")
  Member "on the optimal recovery threshold of coded matrix multiplication":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gcsa codes with noise alignment for secure coded multi party batch matrix multiplication")
  Member "exploitation of stragglers in coded computation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a locality based approach for coded computation")

Question: Please tell me which class the source paper 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)