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 "streamchain do blockchains need blocks"):
  Abstract: Processing at block granularity and blockchains seem inseparable. The original role of blocks is to amortize the cost of cryptography (e.g., solving proof-of-work) and to make data transfers more efficient in a geo-distributed setting. While blocks are simple and powerful tool for amortizing these costs, today in permissioned distributed ledgers, that are often neither geo-distributed, nor require proof-of-work, the benefits of operating on blocks are overshadowed by the large latencies they introduce. #R##N#Our proposal is to switch the distributed ledger processing paradigm from block processing to \emph{stream transaction processing} and rely on batching (i.e., block formation) only for amortizing the cost of disk accesses for commit operations. This paradigm shift enables shaving off end-to-end latencies by more than an order of magnitude and opens up new use-cases for permissioned ledgers. We demonstrate a proof-of-concept of our idea using Hyperledger Fabric, achieving end-to-end latencies of less than 10ms while maintaining relatively high throughput, namely close to 1500 tps.

Includes members:
  Member "hyperledger fabric a distributed operating system for permissioned blockchains":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "tenderbake classical bft style consensus for public blockchains")
  Member "a byzantine fault tolerant ordering service for the hyperledger fabric blockchain platform":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "baash enabling blockchain as a service on high performance computing systems")
  Member "paxos made switch y":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "scaling distributed machine learning with in network aggregation")

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)