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: "c 3 les codes for coded computation that leverage stragglers"
  Abstract: In distributed computing systems, it is well recognized that worker nodes that are slow (called stragglers) tend to dominate the overall job execution time. Coded computation utilizes concepts from erasure coding to mitigate the effect of stragglers by running "coded" copies of tasks comprising a job. Stragglers are typically treated as erasures in this process. While this is useful, there are issues with applying, e.g., MDS codes in a straightforward manner. Specifically, several applications such as matrix-vector products deal with sparse matrices. MDS codes typically require dense linear combinations of submatrices of the original matrix which destroy their inherent sparsity. This is problematic as it results in significantly higher processing times for computing the submatrix-vector products in coded computation. Furthermore, it also ignores partial computations at stragglers. #R##N#In this work, we propose a fine-grained model that quantifies the level of non-trivial coding needed to obtain the benefits of coding in matrix-vector computation. Simultaneously, it allows us to leverage partial computations performed by the straggler nodes. For this model, we propose and evaluate several code designs and discuss their properties.

Connected through hyperedge HE-1 (induced by source paper "timely distributed computation with stragglers"):
  with members:
    - "straggler mitigation at scale"
    - "trading off computation with transmission in status update systems"
    - "age of information with prioritized streams when to buffer preempted packets"
    - "sampling for remote estimation through queues age of information and beyond"
    - "computation scheduling for distributed machine learning with straggling workers"
    - "age of information for computation intensive messages in mobile edge computing"
    - "age of information for discrete time queues"
    - "on timely channel coding with hybrid arq"

Connected through hyperedge HE-2 (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"
    - "universally decodable matrices for distributed matrix vector multiplication"
    - "hierarchical coded computation"
    - "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"
    - "on the optimal recovery threshold of coded matrix multiplication"

Connected through hyperedge HE-3 (induced by source paper "gradient coding with clustering and multi message communication"):
  with members:
    - "computation scheduling for distributed machine learning with straggling workers"
    - "hierarchical coded computation"
    - "speeding up distributed machine learning using codes"
    - "speeding up distributed gradient descent by utilizing non persistent stragglers"
    - "hierarchical coding for distributed computing"
    - "polynomially coded regression optimal straggler mitigation via data encoding"
    - "distributed gradient descent with coded partial gradient computations"
    - "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"
    - "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-5 (induced by source paper "universally decodable matrices for distributed matrix vector multiplication"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "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"
    - "erasure coding for distributed matrix multiplication for matrices with bounded entries"
    - "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 "distributed gradient descent with coded partial gradient computations"):
  with members:
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "computation scheduling for distributed machine learning with straggling workers"
    - "hierarchical coded computation"
    - "speeding up distributed machine learning using codes"
    - "speeding up distributed gradient descent by utilizing non persistent stragglers"
    - "hierarchical coding for distributed computing"
    - "polynomially coded regression optimal straggler mitigation via data encoding"
    - "on the optimal recovery threshold of 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)