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 "using weaker consistency models with monitoring and recovery for improving performance of key value stores"):
  Abstract: Consistency properties provided by most key-value stores can be classified into sequential consistency and eventual consistency. The former is easier to program with but suffers from lower performance whereas the latter suffers from potential anomalies while providing higher performance. We focus on the problem of what a designer should do if he/she has an algorithm that works correctly with sequential consistency but is faced with an underlying key-value store that provides a weaker consistency. We propose a detect-rollback based approach: The designer identifies a correctness predicate, say $P$, and continues to run the protocol, as our system monitors $P$. If $P$ is violated (because of weaker consistency), the system rolls back and resumes the computation at a state where $P$ holds. #R##N#We evaluate this approach with graph-based applications running on the Voldemort key-value store. Our experiments with deployment on Amazon AWS EC2 instances shows that using eventual consistency with monitoring can provide a $50\%$ -- $80\%$ increase in throughput when compared with sequential consistency. We also observe that the overhead of the monitoring itself was low (typically less than $4\%$) and the latency of detecting violations was small. In particular, in a scenario designed to intentionally cause a large number of violations, more than $99.9\%$ of violations were detected in less than 50 milliseconds in regional networks, and in less than 3 seconds in global networks. #R##N#We find that for some applications, frequent rollback can cause the program using eventual consistency to effectively \textit{stall}. We propose alternate mechanisms for dealing with re-occurring rollbacks. Overall, for applications considered in this paper, we find that even with rollback, eventual consistency provides better performance than using sequential consistency.

Includes members:
  Member "consistency models in distributed systems a survey on definitions disciplines challenges and applications":
    also connected through other hyperedges:
      (none)
  Member "the bloom clock":
    also connected through other hyperedges:
      (none)
  Member "on mixing eventual and strong consistency bayou revisited":
    also connected through other hyperedges:
      (none)
  Member "dkvf a framework for rapid prototyping and evaluating distributed key value stores":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "toward adaptive causal consistency for replicated data stores")
  Member "benefit of self stabilizing protocols in eventually consistent key value stores a case study":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "benefits of stabilization versus rollback in eventually consistent key value stores")
  Member "technical report optimistic execution in key value store":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "benefit of self stabilizing protocols in eventually consistent key value stores a case study")
  Member "monitoring partially synchronous distributed systems using smt solvers":
    also connected through other hyperedges:
      (none)
  Member "precision recall and sensitivity of monitoring partially synchronous distributed systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "monitoring partially synchronous distributed systems using smt solvers")

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)