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: "distributed matrix vector multiplication a convolutional coding approach"
  Abstract: Distributed computing systems are well-known to suffer from the problem of slow or failed nodes; these are referred to as stragglers. Straggler mitigation (for distributed matrix computations) has recently been investigated from the standpoint of erasure coding in several works. In this work we present a strategy for distributed matrix-vector multiplication based on convolutional coding. Our scheme can be decoded using a low-complexity peeling decoder. The recovery process enjoys excellent numerical stability as compared to Reed-Solomon coding based approaches (which exhibit significant problems owing their badly conditioned decoding matrices). Finally, our schemes are better matched to the practically important case of sparse matrix-vector multiplication as compared to many previous schemes. Extensive simulation results corroborate our findings.

Connected through hyperedge HE-1 (induced by source paper "straggler resistant distributed matrix computation via coding theory"):
  with members:
    - "numerically stable polynomially coded computing"
    - "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 "numerically stable coded matrix computations via circulant and rotation matrix embeddings"):
  with members:
    - "numerically stable polynomially coded computing"
    - "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-3 (induced by source paper "random convolutional coding for robust and straggler resilient distributed matrix computation"):
  with members:
    - "numerically stable polynomially coded computing"
    - "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"

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)