human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "on the strength of connectivity of inhomogeneous random k out graphs"):
  with members:
    - "k connectivity in random key graphs with unreliable links"
    - "towards k connectivity in heterogeneous sensor networks under pairwise key predistribution"
    - "topological analysis of bitcoin s lightning network"
    - "dandelion lightweight cryptocurrency networking with formal anonymity guarantees"
    - "high throughput cryptocurrency routing in payment channel networks"
    - "k connectivity of inhomogeneous random key graphs with unreliable links"
    - "toward k connectivity of the random graph induced by a pairwise key predistribution scheme with unreliable links"
    - "modeling the pairwise key predistribution scheme in the presence of unreliable links"

Connected through hyperedge HE-2 (induced by source paper "congestion attacks in payment channel networks"):
  with members:
    - "concurrency and privacy with payment channel networks"
    - "hijacking routes in payment channel networks a predictability tradeoff"
    - "topological analysis of bitcoin s lightning network"
    - "discharged payment channels quantifying the lightning network s resilience to topology based attacks"
    - "towards secure and efficient payment channels"
    - "sprites and state channels payment networks that go faster than lightning"

Connected through hyperedge HE-3 (induced by source paper "a cryptoeconomic traffic analysis of bitcoins lightning network"):
  with members:
    - "topological analysis of bitcoin s lightning network"
    - "the evolution of lightning network s topology during its first year and the influence over its core values"
    - "payment networks as creation games"
    - "discharged payment channels quantifying the lightning network s resilience to topology based attacks"
    - "ant routing algorithm for the lightning network"
    - "payment network design with fees"
    - "towards an economic analysis of routing in payment channel networks"
    - "sprites and state channels payment networks that go faster than lightning"

Question: Please tell me which class the center node 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)