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: "gradient coding with clustering and multi message communication"
  Abstract: Gradient descent (GD) methods are commonly employed in machine learning problems to optimize the parameters of the model in an iterative fashion. For problems with massive datasets, computations are distributed to many parallel computing servers (i.e., workers) to speed up GD iterations. While distributed computing can increase the computation speed significantly, the per-iteration completion time is limited by the slowest straggling workers. Coded distributed computing can mitigate straggling workers by introducing redundant computations; however, existing coded computing schemes are mainly designed against persistent stragglers, and partial computations at straggling workers are discarded, leading to wasted computational capacity. In this paper, we propose a novel gradient coding (GC) scheme which allows multiple coded computations to be conveyed from each worker to the master per iteration. We numerically show that the proposed GC with multi-message communication (MMC) together with clustering provides significant improvements in the average completion time (of each iteration), with minimal or no increase in the communication load.

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"
    - "distributed stochastic gradient descent using ldgm codes"
    - "universally decodable matrices for distributed matrix vector multiplication"
    - "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 "weighted gradient coding with leverage score sampling"):
  with members:
    - "numerically stable binary gradient coding"
    - "fundamental limits of approximate gradient coding"
    - "gradient coding based on block designs for mitigating adversarial stragglers"
    - "distributed stochastic gradient descent using ldgm codes"
    - "erasurehead distributed gradient descent without delays using approximate gradient coding"
    - "gradient coding from cyclic mds codes and expander graphs"
    - "balanced reed solomon codes"
    - "lecture notes on randomized linear algebra"

Connected through hyperedge HE-3 (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-4 (induced by source paper "lagc lazily aggregated gradient coding for straggler tolerant and communication efficient distributed learning"):
  with members:
    - "gradient coding based on block designs for mitigating adversarial stragglers"
    - "stochastic gradient coding for straggler mitigation in distributed learning"
    - "erasurehead distributed gradient descent without delays using approximate gradient coding"
    - "speeding up distributed machine learning using codes"
    - "on the optimal recovery threshold of coded 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"
    - "gradient coding from cyclic mds codes and expander graphs"

Connected through hyperedge HE-5 (induced by source paper "mds coding is better than replication for job completion times"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "speeding up distributed gradient descent by utilizing non persistent stragglers"
    - "improving distributed gradient descent using reed solomon codes"
    - "when do redundant requests reduce latency"
    - "on the delay storage trade off in content download from coded distributed storage systems"
    - "the mds queue analysing the latency performance of erasure codes"
    - "network coding meets tcp"

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)