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: "flash efficient dynamic routing for offchain networks"
  Abstract: Offchain networks emerge as a promising solution to address the scalability challenge of blockchain. Participants directly make payments through a network of payment channels without the overhead of committing onchain transactions. Routing is critical to the performance of offchain networks. Existing solutions use either static routing with poor performance or dynamic routing with high overhead to obtain the dynamic channel balance information. In this paper, we propose Flash, a new dynamic routing solution that leverages the unique characteristics of transactions in offchain networks to strike a better tradeoff between path optimality and probing overhead. By studying the traces of real offchain networks, we find that the payment sizes are heavy-tailed, and most payments are highly recurrent. Flash thus differentiates the treatment of elephant payments from mice payments. It uses a modified max-flow algorithm for elephant payments to find paths with sufficient capacity, and strategically routes the payment across paths to minimize the transaction fees. Mice payments are directly sent by looking up a routing table with a few precomputed paths to reduce probing overhead. Testbed experiments and data-driven simulations show that Flash improves the success volume of payments by up to 2.3x compared to the state-of-the-art routing algorithm.

Connected through hyperedge HE-1 (induced by source paper "toward active and passive confidentiality attacks on cryptocurrency off chain networks"):
  with members:
    - "hijacking routes in payment channel networks a predictability tradeoff"
    - "a cryptoeconomic traffic analysis of bitcoins lightning network"
    - "discharged payment channels quantifying the lightning network s resilience to topology based attacks"

Connected through hyperedge HE-2 (induced by source paper "improving sustainability of cryptocurrency payment networks for iot applications"):
  with members:
    - "high throughput cryptocurrency routing in payment channel networks"
    - "settling payments fast and private efficient decentralized routing for path based transactions"

Connected through hyperedge HE-3 (induced by source paper "a secure consensus protocol for sidechains"):
  with members:
    - "selfish mining in ethereum"
    - "proteus a scalable bft consesus protocol for blockchains"
    - "sbft a scalable and decentralized trust infrastructure"
    - "hotstuff bft consensus in the lens of blockchain"
    - "routing cryptocurrency with the spider network"
    - "a scale out blockchain for value transfer with spontaneous sharding"
    - "window based bft blockchain consensus"
    - "sprites and state channels payment networks that go faster than lightning"

Connected through hyperedge HE-4 (induced by source paper "boomerang redundancy improves latency and throughput in payment networks"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "routing cryptocurrency with the spider network"
    - "sprites and state channels payment networks that go faster than lightning"
    - "settling payments fast and private efficient decentralized routing for path based transactions"

Connected through hyperedge HE-5 (induced by source paper "high throughput cryptocurrency routing in payment channel networks"):
  with members:
    - "settling payments fast and private efficient decentralized routing for path based transactions"
    - "a survey and taxonomy of graph sampling"

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.NI(Networking and Internet Architecture)