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 "scaling hyperledger fabric using pipelined execution and sparse peers"):
  Abstract: Many proofs of concept blockchain applications built using Hyperledger Fabric, a permissioned blockchain platform, have recently been transformed into production. However, the performance provided by Hyperledger Fabric is of significant concern for enterprises due to steady growth in network usage. Hence, in this paper, we study the performance achieved in a Fabric network using vertical scaling (i.e., by adding more vCPUs) and horizontal scaling (i.e., by adding more nodes) techniques. We observe that network scales very poorly with both of these techniques. With vertical scaling, due to serial execution of validation & commit phases of transactions, the allocated vCPUs are underutilized. With horizontal scaling, due to redundant work between nodes, allocated resources are wasted though it is utilized. Further, we identify these techniques to be unsuited for dynamically scaling a network quickly to mitigate an overload situation, and hence, it results in a 30% drop in the performance. To increase the CPU utilization and hence the performance, we re-architect Fabric to enable pipelined execution of validation & commit phases by introducing dirty state management using a trie data structure. Additionally, we facilitated the validation phase to validate transactions in parallel by introducing a waiting-transactions dependency graph. To avoid redundant work performed between nodes and to quickly scale up a network, we propose a new type of peer node called sparse peer, which selective commits transactions. Overall, we improved the throughput by 3x and reduced the time taken to scale up a network by 96%.

Includes members:
  Member "blockchain meets database design and implementation of a blockchain relational database":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "trends in development of databases and blockchain")
  Member "fastfabric scaling hyperledger fabric to 20 000 transactions per second":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "xox fabric a hybrid approach to blockchain transaction execution")
  Member "hyperledger fabric a distributed operating system for permissioned blockchains":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "tenderbake classical bft style consensus for public blockchains")
  Member "towards scaling blockchain systems via sharding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "effective scaling of blockchain beyond consensus innovations and moore s law")

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)