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 "sabre protecting bitcoin against routing attacks"):
  Abstract: Routing attacks remain practically effective in the Internet today as existing countermeasures either fail to provide protection guarantees or are not easily deployable. Blockchain systems are particularly vulnerable to such attacks as they rely on Internet-wide communication to reach consensus. In particular, Bitcoin -the most widely-used cryptocurrency- can be split in half by any AS-level adversary using BGP hijacking. In this paper, we present SABRE, a secure and scalable Bitcoin relay network which relays blocks worldwide through a set of connections that are resilient to routing attacks. SABRE runs alongside the existing peer-to-peer network and is easily deployable. As a critical system, SABRE design is highly resilient and can efficiently handle high bandwidth loads, including Denial of Service attacks. We built SABRE around two key technical insights. First, we leverage fundamental properties of inter-domain routing (BGP) policies to host relay nodes: (i) in locations that are inherently protected against routing attacks; and (ii) on paths that are economically preferred by the majority of Bitcoin clients. These properties are generic and can be used to protect other Blockchain-based systems. Second, we leverage the fact that relaying blocks is communication-heavy, not computation-heavy. This enables us to offload most of the relay operations to programmable network hardware (using the P4 programming language). Thanks to this hardware/software co-design, SABRE nodes operate seamlessly under high load while mitigating the effects of malicious clients. We present a complete implementation of SABRE together with an extensive evaluation. Our results demonstrate that SABRE is effective at securing Bitcoin against routing attacks, even with deployments as small as 6 nodes.

Includes members:
  Member "counter raptor safeguarding tor against active routing attacks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deepcorr strong flow correlation attacks on tor using deep learning")
  Member "heavy hitter detection entirely in the data plane":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "constellation a high performance geo distributed middlebox framework")
  Member "hijacking bitcoin routing attacks on cryptocurrencies":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an overview on smart contracts challenges advances and platforms")
  Member "enhancing bitcoin security and performance with strong consistency via collective signing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "surface a practical blockchain consensus algorithm for real world networks")
  Member "holding all the ases identifying and circumventing the pitfalls of as aware tor client design":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "towards an autonomous system monitor for mitigating correlation attacks in the tor network")
  Member "measuring and mitigating as level adversaries against tor":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deepcorr strong flow correlation attacks on tor using deep learning")
  Member "deanonymisation of clients in bitcoin p2p network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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.NI(Networking and Internet Architecture)