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 "towards scaling blockchain systems via sharding"):
  Abstract: Existing blockchain systems scale poorly because of their distributed consensus protocols. Current attempts at improving blockchain scalability are limited to cryptocurrency. Scaling blockchain systems under general workloads (i.e., non-cryptocurrency applications) remains an open question. In this work, we take a principled approach to apply sharding, which is a well-studied and proven technique to scale out databases, to blockchain systems in order to improve their transaction throughput at scale. This is challenging, however, due to the fundamental difference in failure models between databases and blockchain. To achieve our goal, we first enhance the performance of Byzantine consensus protocols, by doing so we improve individual shards' throughput. Next, we design an efficient shard formation protocol that leverages a trusted random beacon to securely assign nodes into shards. We rely on trusted hardware, namely Intel SGX, to achieve high performance for both consensus and shard formation protocol. Third, we design a general distributed transaction protocol that ensures safety and liveness even when transaction coordinators are malicious. Finally, we conduct an extensive evaluation of our design both on a local cluster and on Google Cloud Platform. The results show that our consensus and shard formation protocols outperform state-of-the-art solutions at scale. More importantly, our sharded blockchain reaches a high throughput that can handle Visa-level workloads, and is the largest ever reported in a realistic environment.

Includes members:
  Member "forkbase an efficient storage engine for blockchain and forkable applications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "congestion attacks in payment channel networks")
  Member "chainspace a sharded smart contracts platform":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (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-5 (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-6 (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-7 (induced by source paper "surface a practical blockchain consensus algorithm for real world networks")
  Member "algorand the efficient and democratic ledger":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "consistency of proof of stake blockchains with concurrent honest slot leaders")

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)