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: "tracking in order to recover recoverable lock free data structures"
  Abstract: We present the \emph{tracking approach} for deriving \emph{recoverable} implementations of several widely-used concurrent data structures. Recoverability is appealing for emerging systems featuring byte-addressable \emph{non-volatile main memory} (\emph{NVRAM}), whose durability allows to efficiently resurrect a failed process after it crashes. The tracking approach ensures that after a crash occurs, every executed operation is able to recover and return a correct response, in addition to guaranteeing that the state of the data structure is not corrupted. #R##N#The approach is applicable to lock-free concurrent data structures that use helping and rely on information structures to track the progress of operations. Such a tracking mechanism is already present in a wide range of well-known concurrent data structures, in particular, linked lists, trees and elimination stacks, making it relatively easy #R##N#to derive their recoverable versions using the tracking approach. The tracking approach illustrates that full-fledged logging is not needed and ensures that the progress of concurrent operations is tracked in a \emph{per-process} manner, thus reducing the cost of ensuring recoverability.

Connected through hyperedge HE-1 (induced by source paper "semi asymmetric parallel graph algorithms for nvrams"):
  with members:
    - "pangolin a fault tolerant persistent memory programming library"
    - "write optimized and consistent rdma based nvm systems"
    - "single machine graph analytics on massive datasets using intel optane dc persistent memory"
    - "multiversion concurrency with bounded delay and precise garbage collection"
    - "persistent memory i o primitives"
    - "persistent buffer management with optimistic consistency"
    - "basic performance measurements of the intel optane dc persistent memory module"
    - "theoretically efficient parallel graph algorithms can be fast and scalable"

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)