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: "erasure coding for distributed matrix multiplication for matrices with bounded entries"
  Abstract: Distributed matrix multiplication is widely used in several scientific domains. It is well recognized that computation times on distributed clusters are often dominated by the slowest workers (called stragglers). Recent work has demonstrated that straggler mitigation can be viewed as a problem of designing erasure codes. For matrices $\mathbf A$ and $\mathbf B$, the technique essentially maps the computation of $\mathbf A^T \mathbf B$ into the multiplication of smaller (coded) submatrices. The stragglers are treated as erasures in this process. The computation can be completed as long as a certain number of workers (called the recovery threshold) complete their assigned tasks. #R##N#We present a novel coding strategy for this problem when the absolute values of the matrix entries are sufficiently small. We demonstrate a tradeoff between the assumed absolute value bounds on the matrix entries and the recovery threshold. At one extreme, we are optimal with respect to the recovery threshold and on the other extreme, we match the threshold of prior work. Experimental results on cloud-based clusters validate the benefits of our method.

Connected through hyperedge HE-1 (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"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "universally decodable matrices for distributed matrix vector multiplication"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "c 3 les codes for coded computation that leverage stragglers"
    - "rateless codes for near perfect load balancing in distributed matrix vector multiplication"
    - "erasure correction of scalar codes in the presence of stragglers"
    - "exploitation of stragglers in coded computation"
    - "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-3 (induced by source paper "distributed matrix vector multiplication a convolutional coding approach"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "rateless codes for near perfect load balancing in distributed matrix vector 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-4 (induced by source paper "coded elastic computing"):
  with members:
    - "an application of storage optimal matdot codes for coded matrix multiplication fast k nearest neighbors estimation"
    - "hierarchical coded computation"
    - "learning a code machine learning for approximate non linear coded computation"
    - "speeding up distributed machine learning using codes"
    - "straggler resilient and communication efficient distributed iterative linear solver"
    - "speeding up distributed gradient descent by utilizing non persistent stragglers"
    - "coded computing for distributed graph analytics"
    - "hierarchical coding for distributed computing"

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.DC(Distributed, Parallel, and Cluster Computing)