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: "polynomially coded regression optimal straggler mitigation via data encoding"
  Abstract: We consider the problem of training a least-squares regression model on a large dataset using gradient descent. The computation is carried out on a distributed system consisting of a master node and multiple worker nodes. Such distributed systems are significantly slowed down due to the presence of slow-running machines (stragglers) as well as various communication bottlenecks. We propose "polynomially coded regression" (PCR) that substantially reduces the effect of stragglers and lessens the communication burden in such systems. The key idea of PCR is to encode the partial data stored at each worker, such that the computations at the workers can be viewed as evaluating a polynomial at distinct points. This allows the master to compute the final gradient by interpolating this polynomial. PCR significantly reduces the recovery threshold, defined as the number of workers the master has to wait for prior to computing the gradient. In particular, PCR requires a recovery threshold that scales inversely proportionally with the amount of computation/storage available at each worker. In comparison, state-of-the-art straggler-mitigation schemes require a much higher recovery threshold that only decreases linearly in the per worker computation/storage load. We prove that PCR's recovery threshold is near minimal and within a factor two of the best possible scheme. Our experiments over Amazon EC2 demonstrate that compared with state-of-the-art schemes, PCR improves the run-time by 1.50x ~ 2.36x with naturally occurring stragglers, and by as much as 2.58x ~ 4.29x with artificial stragglers.

Connected through hyperedge HE-1 (induced by source paper "coded federated learning"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "federated learning with non iid data"
    - "coded computation over heterogeneous clusters"

Connected through hyperedge HE-2 (induced by source paper "computation scheduling for distributed machine learning with straggling workers"):
  with members:
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "collaborative machine learning at the wireless edge with blind transmitters"
    - "federated learning over wireless fading channels"
    - "hierarchical coded computation"
    - "speeding up distributed machine learning using codes"
    - "federated learning strategies for improving communication efficiency"
    - "sparse binary compression towards distributed deep learning with minimal communication"
    - "speeding up distributed gradient descent by utilizing non persistent stragglers"

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"
    - "universally decodable matrices for distributed matrix vector multiplication"
    - "hierarchical coded computation"
    - "speeding up distributed machine learning using codes"
    - "c 3 les codes for coded computation that leverage stragglers"
    - "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 "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"
    - "c 3 les codes for coded computation that leverage stragglers"
    - "speeding up distributed gradient descent by utilizing non persistent stragglers"
    - "hierarchical coding for distributed computing"
    - "distributed gradient descent with coded partial gradient computations"
    - "on the optimal recovery threshold of coded matrix multiplication"

Connected through hyperedge HE-5 (induced by source paper "heterogeneity aware gradient coding for straggler tolerance"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "speeding up distributed gradient descent by utilizing non persistent stragglers"
    - "lagrange coded computing optimal design for resiliency security and privacy"
    - "poseidon an efficient communication architecture for distributed deep learning on gpu clusters"
    - "gradient coding from cyclic mds codes and expander graphs"
    - "revisiting distributed synchronous sgd"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "omnivore an optimizer for multi device deep learning on cpus and gpus"

Connected through hyperedge HE-6 (induced by source paper "numerically stable polynomially coded computing"):
  with members:
    - "universally decodable matrices for distributed matrix vector multiplication"
    - "an application of storage optimal matdot codes for coded matrix multiplication fast k nearest neighbors estimation"
    - "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"
    - "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-7 (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"

Connected through hyperedge HE-8 (induced by source paper "staircase pir universally robust private information retrieval"):
  with members:
    - "analyzing the download time of availability codes"
    - "robust private information retrieval from coded systems with byzantine and colluding servers"
    - "speeding up distributed machine learning using codes"
    - "lifting private information retrieval from two to any number of messages"
    - "minimizing latency for secure coded computing using secret sharing via staircase codes"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"
    - "the capacity of private information retrieval from byzantine and colluding databases"
    - "coded convolution for parallel and distributed computing within a deadline"

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)