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: "verifying pram consistency over read write traces of data replicas"
  Abstract: Data replication technologies enable efficient and highly-available data access, thus gaining more and more interests in both the academia and the industry. However, data replication introduces the problem of data consistency. Modern commercial data replication systems often provide weak consistency for high availability under certain failure scenarios. An important weak consistency is Pipelined-RAM (PRAM) consistency. It allows different processes to hold different views of data. To determine whether a data replication system indeed provides PRAM consistency, we study the problem of Verifying PRAM Consistency over read/write traces (or VPC, for short). #R##N#We first identify four variants of VPC according to a) whether there are Multiple shared variables (or one Single variable), and b) whether write operations can assign Duplicate values (or only Unique values) for each shared variable; the four variants are labeled VPC-SU, VPC-MU, VPC-SD, and VPC-MD. Second, we present a simple VPC-MU algorithm, called RW-CLOSURE. It constructs an operation graph $\mathcal{G}$ by iteratively adding edges according to three rules. Its time complexity is $O(n^5)$, where n is the number of operations in the trace. Third, we present an improved VPC-MU algorithm, called READ-CENTRIC, with time complexity $O(n^4)$. Basically it attempts to construct the operation graph $\mathcal{G}$ in an incremental and efficient way. Its correctness is based on that of RW-CLOSURE. Finally, we prove that VPC-SD (so is VPC-MD) is $\sf{NP}$-complete by reducing the strongly $\sf{NP}$-complete problem 3-PARTITION to it.

Connected through hyperedge HE-1 (induced by source paper "on verifying causal consistency"):
  with members:
    - "causal consistency beyond memory"
    - "on reducing linearizability to state reachability"
    - "on the complexity of linearizability"

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)