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: "on the cost of concurrency in transactional memory"
  Abstract: Traditional techniques for synchronization are based on \emph{locking} that provides threads with exclusive access to shared data. \emph{Coarse-grained} locking typically forces threads to access large amounts of data sequentially and, thus, does not fully exploit hardware concurrency. Program-specific \emph{fine-grained} locking or \emph{non-blocking} (\emph{i.e.}, not using locks) synchronization, on the other hand, is a dark art to most programmers and trusted to the wisdom of a few computing experts. Thus, it is appealing to seek a middle ground between these two extremes: a synchronization mechanism that relieves the programmer of the overhead of reasoning about data conflicts that may arise from concurrent operations without severely limiting the program's performance. The \emph{Transactional Memory (TM)} abstraction is proposed as such a mechanism: it intends to combine an easy-to-use programming interface with an efficient utilization of the concurrent-computing abilities provided by multicore architectures. TM allows the programmer to \emph{speculatively} execute sequences of shared-memory operations as \emph{atomic transactions} with \emph{all-or-nothing} semantics: the transaction can either \emph{commit}, in which case it appears as executed sequentially, or \emph{abort}, in which case its update operations do not take effect. Thus, the programmer can design software having only sequential semantics in mind and let TM take care, at run-time, of resolving the conflicts in concurrent executions. #R##N#Intuitively, we want TMs to allow for as much \emph{concurrency} as possible: in the absence of severe data conflicts, transactions should be able to progress in parallel. But what are the inherent costs associated with providing high degrees of concurrency in TMs? This is the central question of the thesis.

Connected through hyperedge HE-1 (induced by source paper "obtaining progress guarantee and greaterconcurrency in multi version object semantics"):
  with members:
    - "achieving starvation freedom in multi version transactional memory systems"
    - "an innovative approach to achieve compositionality efficiently using multi version object based transactional systems"
    - "non interference and local correctness in transactional memory"

Connected through hyperedge HE-2 (induced by source paper "an efficient approach to achieve compositionality using optimized multi version object based transactional systems"):
  with members:
    - "an innovative approach to achieve compositionality efficiently using multi version object based transactional systems"
    - "non interference and local correctness in transactional memory"

Connected through hyperedge HE-3 (induced by source paper "an innovative approach to achieve compositionality efficiently using multi version object based transactional systems"):
  with members:
    - "non interference and local correctness in transactional memory"

Connected through hyperedge HE-4 (induced by source paper "a concurrency optimal binary search tree"):
  with members:
    - "efficient lock free binary search trees"

Connected through hyperedge HE-5 (induced by source paper "why transactional memory should not be obstruction free"):
  with members:
    - "on partial wait freedom in transactional memory"
    - "safety of deferred update in transactional memory"

Connected through hyperedge HE-6 (induced by source paper "progressive transactional memory in time and space"):
  with members:
    - "on partial wait freedom in transactional memory"

Connected through hyperedge HE-7 (induced by source paper "inherent limitations of hybrid transactional memory"):
  with members:
    - "safety of deferred update in transactional memory"

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)