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 "how to profit from payments channels"):
  Abstract: Payment channel networks like Bitcoin's Lightning network are an auspicious approach for realizing high transaction throughput and almost-instant confirmations in blockchain networks. However, the ability to successfully make payments in such networks relies on the willingness of participants to lock collateral in the network. In Lightning, the key financial incentive is to lock collateral are small fees for routing payments for other participants. While users can choose these fees, currently, they mainly stick to the default fees. By providing insights on beneficial choices for fees, we aim to incentivize users to lock more collateral and improve the effectiveness of the network. #R##N#In this paper, we consider a node $\mathbf{A}$ that given the network topology and the channel details selects where to establish channels and how much fee to charge such that its financial gain is maximized. We formalize the optimization problem and show that it is NP-hard. We design a greedy algorithm to approximate the optimal solution. In each step, our greedy algorithm selects a node which maximizes the total reward concerning the number of shortest paths passing through $\mathbf{A}$ and channel fees. Our simulation study leverages real-world data set to quantify the impact of our gain optimization and indicates that our strategy is at least a factor two better than other strategies.

Includes members:
  Member "discharged payment channels quantifying the lightning network s resilience to topology based attacks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "congestion attacks in payment channel networks")
  Member "celer network bring internet scale to every blockchain":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sok of used cryptography in blockchain")
  Member "payment network design with fees":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a cryptoeconomic traffic analysis of bitcoins lightning network")
  Member "towards an economic analysis of routing in payment channel networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "snappy fast on chain payments with practical collaterals")
  Member "sprites and state channels payment networks that go faster than lightning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "congestion attacks in payment channel networks")
  Member "how to charge lightning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a cryptoeconomic traffic analysis of bitcoins lightning network")
  Member "improving the betweenness centrality of a node by adding links":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hijacking routes in payment channel networks a predictability tradeoff")
  Member "settling payments fast and private efficient decentralized routing for path based transactions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "hushrelay a privacy preserving efficient and scalable routing algorithm for off chain payments")

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)