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: "efficient concurrent execution of smart contracts in blockchains using object based transactional memory"
  Abstract: Popular blockchain such as Ethereum and several others execute complex transactions in blocks through user defined scripts known as smart contracts. Normally, a block of the chain consists of multiple transactions of smart contracts which are added by a miner. To append a correct block into blockchain, miners execute these smart contract transactions (SCT) sequentially. Later the validators serially re-execute the SCT of the block. In the current era of multi-core processors, by employing serial execution of the transactions, the miners and validators fail to utilize the cores properly and as a result have poor throughput. By adding concurrency using object semantics to smart contracts execution, we can achieve the better efficiency and higher throughput. Some authors have used read-write STMs (RWSTMs) for the concurrent execution of SCT. Working with higher level operations provide greater concurrency, better throughput and reduces the number of aborts than RWSTMs. In this paper, we develop an efficient framework to execute the SCT concurrently by miner using optimistic Object-Based Software Transactional Memory systems (OSTMs) and Multi-Version OSTMs (MV-OSTM). A proposed block includes SCT, final states of the shared data-items, hash of the previous block and a block graph (BG). BG captures the conflicting relations among the transactions. Later, the validators re-execute the same SCT concurrently and deterministically with the help of BG given by miner to verify the final state. If the validation is successful then proposed block appended into the blockchain and miner gets incentive otherwise discard the proposed block. MV-OSTM and OSTM miner performs 4.5x and 3.86x average speedups over serial miner. Along with, MV-OSTM and OSTM validator outperforms average 32.81x and 29.76x than serial validator.

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.DC(Distributed, Parallel, and Cluster Computing)