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: "multi version coding for consistent distributed storage of correlated data updates"
  Abstract: Motivated by applications of distributed storage systems to cloud-based key-value stores, the multi-version coding problem has been recently formulated to store frequently updated data in asynchronous distributed storage systems. Inspired by consistency requirements in distributed computing, the main goal in multi-version coding is to ensure that the latest possible version of the data is decodable, even if all the data updates have not reached all the servers in the system. In this paper, we study the storage cost of ensuring consistency for the case where the various versions of the data are correlated, in contrast to previous work where versions were treated as being independent. We provide multi-version code constructions that show that the storage cost can be significantly smaller than the previous constructions depending on the degree of correlation between the different versions of the data. Our constructions are based on update-efficient codes, Reed-Solomon code, random binning coding and BCH codes. #R##N#Specifically, we consider the multi-version coding setting with $n$ servers and $\nu$ versions, with a failure tolerance of $n-c$, where the $\nu$ message versions form a Markov chain. The message version is distributed uniformly over all binary vectors of length $K$, and given a version, the subsequent version is uniformly distributed in a Hamming ball of radius $\delta_K K$ centered around that given version. Previously derived achievable schemes have a per-server storage cost of at least $ \frac{1}{2}\frac{\nu K}{c}-o(K)$ when $\nu \leq c$, whereas we propose a scheme that has a storage cost of $\frac{K}{c} + \frac{\nu-1}{c}\log Vol(\delta_KK, K)$, where $Vol(r, K)$ is the volume of a ball of radius $r$ in the $K$ dimensional Hamming cube. Through a converse result, we show that our multi-version code constructions are nearly-optimal in certain correlation regimes.

Connected through hyperedge HE-1 (induced by source paper "codes for updating linear functions over small fields"):
  with members:
    - "updating content in cache aided coded multicast"
    - "communication cost for updating linear functions when message updates are sparse connections to maximally recoverable codes"
    - "vector network coding based on subspace codes outperforms scalar linear network coding"
    - "error correcting functional index codes generalized exclusive laws and graph coloring"
    - "multi version coding an information theoretic perspective of consistent distributed storage"
    - "index coding with coded side information"
    - "fundamental limits on communication for oblivious updates in storage networks"
    - "coding for errors and erasures in random network coding"

Connected through hyperedge HE-2 (induced by source paper "harnessing correlations in distributed erasure coded key value stores"):
  with members:
    - "on coding for cache aided delivery of dynamic correlated content"
    - "storage efficient shared memory emulation"
    - "a layered architecture for erasure coded consistent distributed storage"
    - "communication cost for updating linear functions when message updates are sparse connections to maximally recoverable codes"
    - "correlation aware distributed caching and coded delivery"
    - "information theoretic lower bounds on the storage cost of shared memory emulation"
    - "multi version coding an information theoretic perspective of consistent distributed storage"
    - "compressed differential erasure codes for efficient archival of versioned data"

Connected through hyperedge HE-3 (induced by source paper "multi version coding with side information"):
  with members:
    - "harnessing correlations in distributed erasure coded key value stores"
    - "multi version coding an information theoretic perspective of consistent distributed storage"

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.IT(Information Theory)