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 "private and secure distributed matrix multiplication with flexible communication load"):
  Abstract: Large matrix multiplications are central to large-scale machine learning applications. These operations are often carried out on a distributed computing platform with a master server and multiple workers in the cloud operating in parallel. For such distributed platforms, it has been recently shown that coding over the input data matrices can reduce the computational delay, yielding a trade-off between recovery threshold, i.e., the number of workers required to recover the matrix product, and communication load, i.e., the total amount of data to be downloaded from the workers. In this paper, in addition to exact recovery requirements, we impose security and privacy constraints on the data matrices, and study the recovery threshold as a function of the communication load. We first assume that both matrices contain private information and that workers can collude to eavesdrop on the content of these data matrices. For this problem, we introduce a novel class of secure codes, referred to as secure generalized PolyDot (SGPD) codes, that generalize state-of-the-art non-secure codes for matrix multiplication. SGPD codes allow a flexible trade-off between recovery threshold and communication load for a fixed maximum number of colluding workers while providing perfect secrecy for the two data matrices. We then assume that one of the data matrices is taken from a public set known to all the workers. In this setup, the identity of the matrix of interest should be kept private from the workers. For this model, we present a variant of generalized PolyDot codes that can guarantee both secrecy of one matrix and privacy for the identity of the other matrix for the case of no colluding servers.

Includes members:
  Member "numerically stable polynomially coded computing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "straggler resistant distributed matrix computation via coding theory")
  Member "private sequential function computation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "conditional disclosure of secrets a noise and signal alignment approach")
  Member "random khatri rao product codes for numerically stable distributed matrix multiplication":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "straggler resistant distributed matrix computation via coding theory")
  Member "distributed and private coded matrix computation with flexible communication load":
    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 "private information retrieval with private coded side information the multi server case":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "private computation with individual and joint privacy")
  Member "random convolutional coding for robust and straggler resilient distributed matrix computation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "straggler resistant distributed matrix computation via coding theory")
  Member "secure coded multi party computation for massive matrix operations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gcsa codes with noise alignment for secure coded multi party batch matrix multiplication")
  Member "on the capacity of secure distributed matrix multiplication":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "entangled polynomial codes for secure private and batch distributed matrix multiplication breaking the cubic barrier")

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)