human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "in network redundancy generation for opportunistic speedup of data backup"):
  Abstract: Erasure coding is a storage-efficient alternative to replication for achieving reliable data backup in distributed storage systems. During the storage process, traditional erasure codes require a unique source node to create and upload all the redundant data to the different storage nodes. However, such a source node may have limited communication and computation capabilities, which constrain the storage process throughput. Moreover, the source node and the different storage nodes might not be able to send and receive data simultaneously-e.g., nodes might be busy in a data center setting, or simply be offline in a peer-to-peer setting-which can further threaten the efficacy of the overall storage process. In this paper, we propose an ''in-network'' redundancy generation process which distributes the data insertion load among the source and storage nodes by allowing the storage nodes to generate new redundant data by exchanging partial information among themselves, improving the throughput of the storage process. The process is carried out asynchronously, utilizing spare bandwidth and computing resources from the storage nodes. The proposed approach leverages on the local repairability property of newly proposed erasure codes, tailor made for the needs of distributed storage systems. We analytically show that, the performance of this technique relies on an efficient usage of the spare node resources, and we derive a set of scheduling algorithms to maximize the same. We experimentally show, using availability traces from real peer-to-peer applications as well as Google data center availability and workload traces, that, our algorithms can, depending on the environmental characteristics, increase the throughput of the storage process significantly (up to 90% in data centers, and 60% in peer-to-peer settings) with respect to the classical naive data insertion approach.

Includes members:
  Member "on locality in distributed storage systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "repairing generalized reed muller codes")
  Member "cooperative regenerating codes for distributed storage systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "convertible codes efficient conversion of coded data in distributed storage")
  Member "repairing multiple failures with coordinated and adaptive regenerating codes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "new constructions of cooperative msr codes reducing node size to exp o n")
  Member "self repairing homomorphic codes for distributed storage systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "new bounds on the field size for maximally recoverable codes instantiating grid like topologies")

Question: Please tell me which class the source paper 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)