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 "convertible codes efficient conversion of coded data in distributed storage"):
  Abstract: Large-scale distributed storage systems typically use erasure codes to provide durability of data in the face of failures. A set of $k$ blocks to be stored is encoded using an $[n, k]$ code to generate $n$ blocks that are then stored on different storage nodes. The redundancy configuration is chosen based on the failure rates of storage devices, and is typically kept constant. However, a recent work by Kadekodi et al. shows that the failure rate of storage devices vary significantly over time, and that adapting the redundancy configuration in response to such variations provides significant benefits. Converting the redundancy configuration of already encoded data by re-encoding requires significant overhead on resources such as accesses, device IO, network bandwidth, and compute cycles. #R##N#In this work, we first present a framework to formalize the notion of code conversion: the process of converting data encoded with an $[n^I, k^I]$ code into data encoded with an $[n^F, k^F]$ code while maintaining desired decodability properties, such as the maximum-distance-separable (MDS) property. We then introduce convertible codes, a new class of codes that allow for code conversions in a resource-efficient manner. For an important parameter regime (which we call the merge regime) along with the linearity and MDS decodability constraint, we prove tight bounds on the number of nodes accessed during code conversion. In particular, our achievability result is an explicit construction of MDS convertible codes that are optimal for all parameter values in the merge regime albeit with a high field size. We then present explicit low-field-size constructions of optimal MDS convertible codes for a broad range of parameters in the merge regime. Our results thus show that it is indeed possible to achieve code conversions with significantly lesser resources as compared to re-encoding.

Includes members:
  Member "an exponential lower bound on the sub packetization of msr codes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "near optimal repair of reed solomon codes with low sub packetization")
  Member "combinatorial alphabet dependent bounds for locally recoverable codes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "locally recoverable j affine variety codes")
  Member "capacity of locally recoverable codes":
    also connected through other hyperedges:
      (none)
  Member "a layered architecture for erasure coded consistent distributed storage":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "harnessing correlations in distributed erasure coded key value stores")
  Member "product matrix msr codes with bandwidth adaptive exact repair":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cascade codes for distributed storage systems")
  Member "bandwidth adaptive error resilient mbr exact repair regenerating codes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "towards practical private information retrieval from mds array codes")
  Member "the storage vs repair bandwidth trade off for multiple failures in clustered storage networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "capacity of distributed storage systems with clusters and separate nodes")
  Member "locally recoverable codes from algebraic curves and surfaces":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "universal and dynamic locally repairable codes with maximal recoverability via sum rank codes")

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.IT(Information Theory)