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: "an accelerated method for message propagation in blockchain networks"
  Abstract: Blockchain is based on a P2P network, supporting decentralized consensus of current cryptocurrencies. Since bitcoin and altcoins all utilize an underlying blockchain, they are therefore greatly affected by the performance of the P2P network. In bitcoin, the miners are engaged in a time-sensitive competition to solve a Proof-of-Work problem to extend the blockchain. This consequently raises a critical problem to minimize the time between the propagation of a winning block and the beginning of the next round of the competition. This paper proposes a method that selects a node's closest neighbors to make messages propagate in the whole network in time. The method measures the distance from a node to its neighbor using transmission latency; thus, the lower the latency, the closer the neighbor. Simulations showed a good rate of decrease in average propagation latency and maximum propagation latency, compared to the classic method. Furthermore, this paper not only proposes the principle of establishing connections based on latency, but also evaluates the influence of the number of simultaneously established connections.

Connected through hyperedge HE-1 (induced by source paper "mtfs merkle tree based file system"):
  with members:
    - "boost blockchain broadcast propagation with tree routing"
    - "improve blockchain performance using graph data structure and parallel mining"
    - "ipfs content addressed versioned p2p file system"

Connected through hyperedge HE-2 (induced by source paper "boost blockchain broadcast propagation with tree routing"):
  with members:
    - "improve blockchain performance using graph data structure and parallel mining"

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)