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: "numerically stable polynomially coded computing"
  Abstract: We study the numerical stability of polynomial based encoding methods, which has emerged to be a powerful class of techniques for providing straggler and fault tolerance in the area of coded computing. Our contributions are as follows: 1) We construct new codes for matrix multiplication that achieve the same fault/straggler tolerance as the previously constructed MatDot Codes and Polynomial Codes. Unlike previous codes that use polynomials expanded in a monomial basis, our codes uses a basis of orthogonal polynomials. 2) We show that the condition number of every $m \times m$ sub-matrix of an $m \times n, n \geq m$ Chebyshev-Vandermonde matrix, evaluated on the $n$-point Chebyshev grid, grows as $O(n^{2(n-m)})$ for $n > m$. An implication of this result is that, when Chebyshev-Vandermonde matrices are used for coded computing, for a fixed number of redundant nodes $s=n-m,$ the condition number grows at most polynomially in the number of nodes $n$. 3) By specializing our orthogonal polynomial based constructions to Chebyshev polynomials, and using our condition number bound for Chebyshev-Vandermonde matrices, we construct new numerically stable techniques for coded matrix multiplication. We empirically demonstrate that our constructions have significantly lower numerical errors compared to previous approaches which involve inversion of Vandermonde matrices. We generalize our constructions to explore the trade-off between computation/communication and fault-tolerance. 4) We propose a numerically stable specialization of Lagrange coded computing. Motivated by our condition number bound, our approach involves the choice of evaluation points and a suitable decoding procedure that involves inversion of an appropriate Chebyshev-Vandermonde matrix. Our approach is demonstrated empirically to have lower numerical errors as compared to standard methods.

Connected through hyperedge HE-1 (induced by source paper "straggler resistant distributed matrix computation via coding theory"):
  with members:
    - "distributed matrix vector multiplication a convolutional coding approach"
    - "random khatri rao product codes for numerically stable distributed matrix multiplication"
    - "random convolutional coding for robust and straggler resilient distributed matrix computation"
    - "numerically stable coded matrix computations via circulant and rotation matrix embeddings"
    - "universally decodable matrices for distributed matrix vector multiplication"
    - "speeding up distributed machine learning using codes"
    - "straggler mitigation in distributed matrix multiplication fundamental limits and optimal coding"
    - "on the optimal recovery threshold of coded matrix multiplication"

Connected through hyperedge HE-2 (induced by source paper "private and secure distributed matrix multiplication with flexible communication load"):
  with members:
    - "private sequential function computation"
    - "random khatri rao product codes for numerically stable distributed matrix multiplication"
    - "distributed and private coded matrix computation with flexible communication load"
    - "private information retrieval with private coded side information the multi server case"
    - "random convolutional coding for robust and straggler resilient distributed matrix computation"
    - "secure coded multi party computation for massive matrix operations"
    - "on the capacity of secure distributed matrix multiplication"
    - "private secure coded computation"

Connected through hyperedge HE-3 (induced by source paper "numerically stable coded matrix computations via circulant and rotation matrix embeddings"):
  with members:
    - "distributed matrix vector multiplication a convolutional coding approach"
    - "random khatri rao product codes for numerically stable distributed matrix multiplication"
    - "random convolutional coding for robust and straggler resilient distributed matrix computation"
    - "universally decodable matrices for distributed matrix vector multiplication"
    - "speeding up distributed machine learning using codes"
    - "c 3 les codes for coded computation that leverage stragglers"
    - "straggler mitigation in distributed matrix multiplication fundamental limits and optimal coding"
    - "on the optimal recovery threshold of coded matrix multiplication"

Connected through hyperedge HE-4 (induced by source paper "random khatri rao product codes for numerically stable distributed matrix multiplication"):
  with members:
    - "universally decodable matrices for distributed matrix vector multiplication"
    - "speeding up distributed machine learning using codes"
    - "a unified coded deep neural network training strategy based on generalized polydot codes"
    - "straggler mitigation in distributed matrix multiplication fundamental limits and optimal coding"
    - "lagrange coded computing optimal design for resiliency security and privacy"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"
    - "short dot computing large linear transforms distributedly using coded short dot products"
    - "a unified coding framework for distributed computing with straggling servers"

Connected through hyperedge HE-5 (induced by source paper "random convolutional coding for robust and straggler resilient distributed matrix computation"):
  with members:
    - "distributed matrix vector multiplication a convolutional coding approach"
    - "universally decodable matrices for distributed matrix vector multiplication"
    - "speeding up distributed machine learning using codes"
    - "straggler mitigation in distributed matrix multiplication fundamental limits and optimal coding"
    - "rateless codes for near perfect load balancing in distributed matrix vector multiplication"
    - "coded sparse matrix multiplication"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"
    - "short dot computing large linear transforms distributedly using coded short dot products"

Connected through hyperedge HE-6 (induced by source paper "factored lt and factored raptor codes for large scale distributed matrix multiplication"):
  with members:
    - "random khatri rao product codes for numerically stable distributed matrix multiplication"
    - "speeding up distributed machine learning using codes"
    - "straggler mitigation in distributed matrix multiplication fundamental limits and optimal coding"
    - "coded sparse matrix multiplication"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"

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)