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: "combating computational heterogeneity in large scale distributed computing via work exchange"
  Abstract: Owing to data-intensive large-scale applications, distributed computation systems have gained significant recent interest, due to their ability of running such tasks over a large number of commodity nodes in a time efficient manner. One of the major bottlenecks that adversely impacts the time efficiency is the computational heterogeneity of distributed nodes, often limiting the task completion time due to the slowest worker. #R##N#In this paper, we first present a lower bound on the expected computation time based on the work-conservation principle. We then present our approach of work exchange to combat the latency problem, in which faster workers can be reassigned additional leftover computations that were originally assigned to slower workers. We present two variations of the work exchange approach: a) when the computational heterogeneity knowledge is known a priori; and b) when heterogeneity is unknown and is estimated in an online manner to assign tasks to distributed workers. As a baseline, we also present and analyze the use of an optimized Maximum Distance Separable (MDS) coded distributed computation scheme over heterogeneous nodes. Simulation results also compare the proposed approach of work exchange, the baseline MDS coded scheme and the lower bound obtained via work-conservation principle. We show that the work exchange scheme achieves time for computation which is very close to the lower bound with limited coordination and communication overhead even when the knowledge about heterogeneity levels is not available.

Connected through hyperedge HE-1 (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-2 (induced by source paper "wireless mapreduce distributed computing"):
  with members:
    - "leveraging coding techniques for speeding up distributed computing"
    - "speeding up distributed machine learning using codes"
    - "federated learning strategies for improving communication efficiency"
    - "distributed computing with heterogeneous communication constraints the worst case computation load and proof by contradiction"
    - "coded computing for distributed graph analytics"
    - "hierarchical coding for distributed computing"
    - "compressed coded distributed computing"
    - "coded distributed computing with node cooperation substantially increases speedup factors"

Connected through hyperedge HE-3 (induced by source paper "near optimal coded data shuffling for distributed learning"):
  with members:
    - "the exact rate memory tradeoff for caching with uncoded prefetching"
    - "speeding up distributed machine learning using codes"
    - "on the fundamental limits of coded data shuffling for distributed learning systems"
    - "a pliable index coding approach to data shuffling"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"
    - "low subpacketization schemes for coded caching"
    - "latency analysis of coded computation schemes over wireless networks"
    - "communication vs distributed computation an alternative trade off curve"

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)