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 "optimum transmission delay for function computation in nfv based networks the role of network coding and redundant computing"):
  Abstract: In this paper, we study the problem of delay minimization in NFV-based networks. In such systems, the ultimate goal of any request is to compute a sequence of functions in the network, where each function can be computed at only a specific subset of network nodes. In conventional approaches, for each function, we choose one node from the corresponding subset of the nodes to compute that function. In contrast, in this work, we allow each function to be computed in more than one node, redundantly in parallel, to respond to a given request. We argue that such redundancy in computation not only improves the reliability of the network, but would also, perhaps surprisingly, reduce the overall transmission delay. In particular, we establish that by judiciously choosing the subset of nodes which compute each function, in conjunction with a linear network coding scheme to deliver the result of each computation, we can characterize and achieve the optimal end-to-end transmission delay. In addition, we show that using such technique, we can significantly reduce the transmission delay as compared to the conventional approach. In some scenarios, such reduction can even scale with the size of the network. More precisely, by increasing the number of nodes that can compute the given function in parallel by a multiplicative factor, the end-to-end delay will also decrease by the same factor. Moreover, we show that while finding the subset of nodes for each computation, in general, is a complex integer program, approximation algorithms can be proposed to reduce the computational complexity. In fact, for the case where the number of computing nodes for a given function is upper-bounded by a constant, a dynamic programming scheme can be proposed to find the optimum subsets in polynomial times. Our numerical simulations confirm the achieved gain in performance in comparison with conventional approaches.

Includes members:
  Member "polynomial codes an optimal design for high dimensional coded matrix multiplication":
    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 "gradient coding from cyclic mds codes and expander graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a locality based approach for coded computation")
  Member "coded computation against processing delays for virtualized cloud based channel decoding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "codenet training large scale neural networks in presence of soft errors")
  Member "coded fourier transform":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "gcsa codes with noise alignment for secure coded multi party batch matrix multiplication")
  Member "a fundamental tradeoff between computation and communication in distributed computing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "subexponential and linear subpacketization coded caching via line graphs and projective geometry")
  Member "coded network function virtualization fault tolerance via in network coding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "coded computation against processing delays for virtualized cloud based channel decoding")
  Member "a scalable framework for wireless distributed computing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "coded distributed computing schemes with smaller numbers of input files and output functions")
  Member "specifying and placing chains of virtual network functions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a virtual network customization framework for multicast services in nfv enabled core networks")

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)