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 "chain of trust can trusted hardware help scaling blockchains"):
  Abstract: As blockchain systems proliferate, there remains an unresolved scalability problem of their underlying distributed consensus protocols. Byzantine Fault Tolerance (BFT) consensus protocols offer high transaction throughput, but can only support small networks. Proof-of-Work (PoW) consensus protocol, on the other hand, can support thousands of nodes, but at the expense of poor transaction throughput. Two potential approaches to address these scalability barriers are by relaxing the threat model, or employing sharding technique to deal with large networks. Nonetheless, their effectiveness against data-intensive blockchain workloads remains to be seen. #R##N#In this work, we study the use and effectiveness of trusted hardware on scaling distributed consensus protocols, and by their extension, blockchain systems. We first analyze existing approaches that harness trusted hardware to enhance scalability, and identify their limitations. Drawing insights from these results, we propose two design principles, namely scale up by scaling down and prioritize consensus messages, that enable the consensus protocols to scale. We illustrate the two principles by presenting optimizations that improve upon state-of-the-art solutions, and demonstrate via our extensive evaluations that they indeed offer better scalability. In particular, integrating our optimizations into Hyperledger Fabric achieves up to 7x higher throughput, while enabling the system to remain operational as the network size increases. Another optimization that we introduce to Hyperledger Sawtooth allows the system to sustain high throughput as the network grows. Finally, our new design for sharding protocols reduces the cost of shard creation phase by up to 35x.

Includes members:
  Member "decentralization in bitcoin and ethereum networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "stability and scalability of blockchain systems")
  Member "forkbase an efficient storage engine for blockchain and forkable applications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a blueprint for interoperable blockchains")
  Member "sprites and state channels payment networks that go faster than lightning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "congestion attacks in payment channel networks")
  Member "chainspace a sharded smart contracts platform":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "surface a practical blockchain consensus algorithm for real world networks")
  Member "adding concurrency to smart contracts":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an overview on smart contracts challenges advances and platforms")
  Member "blockbench a framework for analyzing private blockchains":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "effective scaling of blockchain beyond consensus innovations and moore s law")
  Member "untangling blockchain a data processing view of blockchain systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "effective scaling of blockchain beyond consensus innovations and moore s law")
  Member "enhancing bitcoin security and performance with strong consistency via collective signing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "surface a practical blockchain consensus algorithm for real world networks")

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)