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 "numerically stable coded matrix computations via circulant and rotation matrix embeddings"):
  Abstract: Several recent works have used coding-theoretic ideas for mitigating the effect of stragglers in distributed matrix computations (matrix-vector and matrix-matrix multiplication) over the reals. In particular, a polynomial code based approach distributes matrix-matrix multiplication among $n$ worker nodes by means of polynomial evaluations. This allows for an ``optimal'' recovery threshold whereby the intended result can be decoded as long as at least $(n-s)$ worker nodes complete their tasks; $s$ is the number of stragglers that the scheme can handle. However, a major issue with these approaches is the high condition number of the corresponding Vandermonde-structured recovery matrices. This presents serious numerical precision issues when decoding the desired result. #R##N#It is well known that the condition number of real Vandermonde matrices grows exponentially in $n$. In contrast, the condition numbers of Vandermonde matrices with parameters on the unit circle are much better behaved. In this work we leverage the properties of circulant permutation matrices and rotation matrices to obtain coded computation schemes with significantly lower worst case condition numbers; these matrices have eigenvalues that lie on the unit circle. Our scheme is such that the associated recovery matrices have a condition number corresponding to Vandermonde matrices with parameters given by the eigenvalues of the corresponding circulant permutation and rotation matrices. We demonstrate an upper bound on the worst case condition number of these matrices which grows as $\approx O(n^{s+6})$. In essence, we leverage the well-behaved conditioning of complex Vandermonde matrices with parameters on the unit circle, while still working with computation over the reals. Experimental results demonstrate that our proposed method has condition numbers that are several orders of magnitude better than prior work.

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 "distributed matrix vector multiplication a convolutional coding approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "straggler resistant distributed matrix computation via coding theory")
  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 "random convolutional coding for robust and straggler resilient distributed matrix computation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "straggler resistant distributed matrix computation via coding theory")
  Member "universally decodable matrices for distributed matrix vector multiplication":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "numerically stable binary gradient coding")
  Member "speeding up distributed machine learning using codes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "gcsa codes with noise alignment for secure coded multi party batch matrix multiplication")
  Member "c 3 les codes for coded computation that leverage stragglers":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "timely distributed computation with stragglers")
  Member "straggler mitigation in distributed matrix multiplication fundamental limits and optimal coding":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "gcsa codes with noise alignment for secure coded multi party batch matrix multiplication")

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)