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: "universally decodable matrices for distributed matrix vector multiplication"
  Abstract: Coded computation is an emerging research area that leverages concepts from erasure coding to mitigate the effect of stragglers (slow nodes) in distributed computation clusters, especially for matrix computation problems. In this work, we present a class of distributed matrix-vector multiplication schemes that are based on codes in the Rosenbloom-Tsfasman metric and universally decodable matrices. Our schemes take into account the inherent computation order within a worker node. In particular, they allow us to effectively leverage partial computations performed by stragglers (a feature that many prior works lack). An additional main contribution of our work is a companion matrix-based embedding of these codes that allows us to obtain sparse and numerically stable schemes for the problem at hand. Experimental results confirm the effectiveness of our techniques.

Connected through hyperedge HE-1 (induced by source paper "numerically stable binary gradient coding"):
  with members:
    - "fundamental limits of approximate gradient coding"
    - "coded distributed computing performance limits and code designs"
    - "gradient coding based on block designs for mitigating adversarial stragglers"
    - "gradient coding with clustering and multi message communication"
    - "distributed stochastic gradient descent using ldgm codes"
    - "erasurehead distributed gradient descent without delays using approximate gradient coding"
    - "speeding up distributed machine learning using codes"
    - "rateless codes for near perfect load balancing in distributed matrix vector multiplication"

Connected through hyperedge HE-2 (induced by source paper "straggler resistant distributed matrix computation via coding theory"):
  with members:
    - "numerically stable polynomially coded computing"
    - "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"
    - "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-3 (induced by source paper "hierarchical coded matrix multiplication"):
  with members:
    - "computation scheduling for distributed machine learning with straggling workers"
    - "cuboid partitioning for hierarchical coded matrix multiplication"
    - "hierarchical coded computation"
    - "speeding up distributed machine learning using codes"
    - "c 3 les codes for coded computation that leverage stragglers"
    - "polynomially coded regression optimal straggler mitigation via data encoding"
    - "a unified coded deep neural network training strategy based on generalized polydot codes"
    - "on the optimal recovery threshold of coded matrix multiplication"

Connected through hyperedge HE-4 (induced by source paper "numerically stable coded matrix computations via circulant and rotation matrix embeddings"):
  with members:
    - "numerically stable polynomially coded computing"
    - "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"
    - "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-5 (induced by source paper "numerically stable polynomially coded computing"):
  with members:
    - "an application of storage optimal matdot codes for coded matrix multiplication fast k nearest neighbors estimation"
    - "speeding up distributed machine learning using codes"
    - "polynomially coded regression optimal straggler mitigation via data encoding"
    - "a unified coded deep neural network training strategy based on generalized polydot codes"
    - "straggler mitigation in distributed matrix multiplication fundamental limits and optimal coding"
    - "on the optimal recovery threshold of coded matrix multiplication"
    - "lagrange coded computing optimal design for resiliency security and privacy"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"

Connected through hyperedge HE-6 (induced by source paper "collaborative decoding of polynomial codes for distributed computation"):
  with members:
    - "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"
    - "collaborative decoding of interleaved reed solomon codes and concatenated code designs"

Connected through hyperedge HE-7 (induced by source paper "random khatri rao product codes for numerically stable distributed matrix multiplication"):
  with members:
    - "numerically stable polynomially coded computing"
    - "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-8 (induced by source paper "random convolutional coding for robust and straggler resilient distributed matrix computation"):
  with members:
    - "numerically stable polynomially coded computing"
    - "distributed matrix vector multiplication a convolutional coding approach"
    - "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"

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)