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 "privacy utility tradeoffs in routing cryptocurrency over payment channel networks"):
  Abstract: Payment channel networks (PCNs) are viewed as one of the most promising scalability solutions for cryptocurrencies today. Roughly, PCNs are networks where each node represents a user and each directed, weighted edge represents funds escrowed on a blockchain; these funds can be transacted only between the endpoints of the edge. Users efficiently transmit funds from node A to B by relaying them over a path connecting A to B, as long as each edge in the path contains enough balance (escrowed funds) to support the transaction. Whenever a transaction succeeds, the edge weights are updated accordingly. However, in deployed PCNs, channel balances (i.e., edge weights) are not revealed to users for privacy reasons; users know only the initial weights at time 0. Hence, when routing transactions, users first guess a path, then check if it supports the transaction. This guess-and-check process dramatically reduces the success rate of transactions. At the other extreme, knowing full channel balances can give substantial improvements in success rate at the expense of privacy. In this work, we study whether a network can reveal noisy channel balances to trade off privacy for utility. We show fundamental limits on such a tradeoff, and propose noise mechanisms that achieve the fundamental limit for a general class of graph topologies. Our results suggest that in practice, PCNs should operate either in the low-privacy or low-utility regime; it is not possible to get large gains in utility by giving up a little privacy, or large gains in privacy by sacrificing a little utility.

Includes members:
  Member "concurrency and privacy with payment channel networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hushrelay a privacy preserving efficient and scalable routing algorithm for off chain payments")
  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 "routing cryptocurrency with the spider network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hushrelay a privacy preserving efficient and scalable routing algorithm for off chain payments")
  Member "ohie blockchain scaling made simple":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "incentive analysis of bitcoin ng revisited")
  Member "high throughput cryptocurrency routing in payment channel networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the strength of connectivity of inhomogeneous random k out graphs")
  Member "dandelion redesigning the bitcoin network for anonymity":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "stability and scalability of blockchain systems")
  Member "settling payments fast and private efficient decentralized routing for path based transactions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hushrelay a privacy preserving efficient and scalable routing algorithm for off chain payments")
  Member "deanonymisation of clients in bitcoin p2p network":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "characterizing eosio blockchain")

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.CR(Cryptography and Security)