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: "computation scheduling for distributed machine learning with straggling workers"
  Abstract: We study scheduling of computation tasks across   $n$   workers in a large scale distributed learning problem with the help of a master. Computation and communication delays are assumed to be random, and redundant computations are assigned to workers in order to tolerate stragglers. We consider sequential computation of tasks assigned to a worker, while the result of each computation is sent to the master right after its completion. Each computation round, which can model an iteration of the stochastic gradient descent (SGD) algorithm, is  completed  once the master receives   $k$   distinct computations, referred to as the  computation target . Our goal is to characterize the  average completion time  as a function of the  computation load , which denotes the portion of the dataset available at each worker, and the computation target. We propose two computation scheduling schemes that specify the tasks assigned to each worker, as well as their computation schedule, i.e., the order of execution. Assuming a general statistical model for computation and communication delays, we derive the average completion time of the proposed schemes. We also establish a lower bound on the minimum average completion time by assuming prior knowledge of the random delays. Experimental results carried out on Amazon EC2 cluster show a significant reduction in the average completion time over existing coded and uncoded computing schemes. It is also shown numerically that the gap between the proposed scheme and the lower bound is relatively small, confirming the efficiency of the proposed scheduling design.

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"
    - "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"
    - "mds coding is better than replication for job completion times"

Connected through hyperedge HE-2 (induced by source paper "machine learning at the wireless edge distributed stochastic gradient descent over the air"):
  with members:
    - "hierarchical coded computation"
    - "wireless network intelligence at the edge"
    - "speeding up distributed machine learning using codes"
    - "federated learning strategies for improving communication efficiency"
    - "sparcml high performance sparse communication for machine learning"
    - "broadband analog aggregation for low latency federated edge learning extended version"
    - "federated learning via over the air computation"
    - "sparse binary compression towards distributed deep learning with minimal communication"

Connected through hyperedge HE-3 (induced by source paper "hierarchical coded matrix multiplication"):
  with members:
    - "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"
    - "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-4 (induced by source paper "gradient coding with clustering and multi message communication"):
  with members:
    - "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"
    - "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-5 (induced by source paper "heterogeneous coded computation across heterogeneous workers"):
  with members:
    - "hierarchical coded computation"
    - "wireless network intelligence at the edge"
    - "speeding up distributed machine learning using codes"
    - "speeding up distributed gradient descent by utilizing non persistent stragglers"
    - "on the effect of task to worker assignment in distributed computing systems with stragglers"
    - "on the optimal recovery threshold of coded matrix multiplication"
    - "coded computation over heterogeneous clusters"
    - "near optimal straggler mitigation for distributed gradient methods"

Connected through hyperedge HE-6 (induced by source paper "collaborative machine learning at the wireless edge with blind transmitters"):
  with members:
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "federated learning over wireless fading channels"
    - "broadband analog aggregation for low latency federated edge learning extended version"
    - "federated learning via over the air computation"
    - "don t use large mini batches use local sgd"
    - "deep learning with limited numerical precision"
    - "adam a method for stochastic optimization"
    - "scaling up mimo opportunities and challenges with very large arrays"

Connected through hyperedge HE-7 (induced by source paper "federated echo state learning for minimizing breaks in presence in wireless virtual reality networks"):
  with members:
    - "distributed federated learning for ultra reliable low latency vehicular communications"
    - "towards federated learning at scale system design"
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "communication computing and caching for mobile vr delivery modeling and trade off"
    - "learning to communicate in uav aided wireless networks map based approaches"
    - "edge computing meets millimeter wave enabled vr paving the way to cutting the cord"
    - "analysis of memory capacity for deep echo state networks"
    - "human in the loop wireless communications machine learning and brain aware resource management"

Connected through hyperedge HE-8 (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"
    - "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"
    - "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.DC(Distributed, Parallel, and Cluster Computing)