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: "on throughput delay optimal access to storage clouds via load adaptive coding and chunking"
  Abstract: Recent literature including our past work provide analysis and solutions for using (i) erasure coding, (ii) parallelism, or (iii) variable slicing/chunking (i.e., dividing an object of a specific size into a variable number of smaller chunks) in speeding the I/O performance of storage clouds. However, a comprehensive approach that considers all three dimensions together to achieve the best throughput-delay trade-off curve had been lacking. This paper presents the first set of solutions that can pick the best combination of coding rate and object chunking/slicing options as the load dynamically changes. Our specific contributions are as follows: (1) We establish via measurement that combining variable coding rate and chunking is mostly feasible over a popular public cloud. (2) We relate the delay optimal values for chunking level and code rate to the queue backlogs via an approximate queueing analysis. (3) Based on this analysis, we propose TOFEC that adapts the chunking level and coding rate against the queue backlogs. Our trace-driven simulation results show that TOFEC's adaptation mechanism converges to an appropriate code that provides the optimal throughput-delay trade-off without reducing system capacity. Compared to a non-adaptive strategy optimized for throughput, TOFEC delivers $2.5\times$ lower latency under light workloads; compared to a non-adaptive strategy optimized for latency, TOFEC can scale to support over $3\times$ as many requests. (4) We propose a simpler greedy solution that performs on a par with TOFEC in average delay performance, but exhibits significantly more performance variations.

Connected through hyperedge HE-1 (induced by source paper "when queueing meets coding optimal latency data retrieving scheme in storage clouds"):
  with members:
    - "when do redundant requests reduce latency"
    - "fast cloud pushing the envelope on delay performance of cloud storage with coding"
    - "on the delay storage trade off in content download from coded distributed storage systems"
    - "toward sustainable networking storage area networks with network coding"
    - "the mds queue analysing the latency performance of erasure codes"
    - "a survey on network codes for distributed storage"
    - "on the delay of network coding over line networks"

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.NI(Networking and Internet Architecture)