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: "asap asynchronous approximate data parallel computation"
  Abstract: Emerging workloads, such as graph processing and machine learning are approximate because of the scale of data involved and the stochastic nature of the underlying algorithms. These algorithms are often distributed over multiple machines using bulk-synchronous processing (BSP) or other synchronous processing paradigms such as map-reduce. However, data parallel processing primitives such as repeated barrier and reduce operations introduce high synchronization overheads. Hence, many existing data-processing platforms use asynchrony and staleness to improve data-parallel job performance. Often, these systems simply change the synchronous communication to asynchronous between the worker nodes in the cluster. This improves the throughput of data processing but results in poor accuracy of the final output since different workers may progress at different speeds and process inconsistent intermediate outputs. #R##N#In this paper, we present ASAP, a model that provides asynchronous and approximate processing semantics for data-parallel computation. ASAP provides fine-grained worker synchronization using NOTIFY-ACK semantics that allows independent workers to run asynchronously. ASAP also provides stochastic reduce that provides approximate but guaranteed convergence to the same result as an aggregated all-reduce. In our results, we show that ASAP can reduce synchronization costs and provides 2-10X speedups in convergence and up to 10X savings in network costs for distributed machine learning applications and provides strong convergence guarantees.

Connected through hyperedge HE-1 (induced by source paper "hop heterogeneity aware decentralized training"):
  with members:
    - "communication compression for decentralized training"
    - "revisiting distributed synchronous sgd"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "tensorflow a system for large scale machine learning"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "dimmwitted a study of main memory statistical analytics"

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)