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: "the lock free k lsm relaxed priority queue"
  Abstract: Priority queues are data structures which store keys in an ordered fashion to allow efficient access to the minimal (maximal) key. Priority queues are essential for many applications, e.g., Dijkstra's single-source shortest path algorithm, branch-and-bound algorithms, and prioritized schedulers. #R##N#Efficient multiprocessor computing requires implementations of basic data structures that can be used concurrently and scale to large numbers of threads and cores. Lock-free data structures promise superior scalability by avoiding blocking synchronization primitives, but the \emph{delete-min} operation is an inherent scalability bottleneck in concurrent priority queues. Recent work has focused on alleviating this obstacle either by batching operations, or by relaxing the requirements to the \emph{delete-min} operation. #R##N#We present a new, lock-free priority queue that relaxes the \emph{delete-min} operation so that it is allowed to delete \emph{any} of the $\rho+1$ smallest keys, where $\rho$ is a runtime configurable parameter. Additionally, the behavior is identical to a non-relaxed priority queue for items added and removed by the same thread. The priority queue is built from a logarithmic number of sorted arrays in a way similar to log-structured merge-trees. We experimentally compare our priority queue to recent state-of-the-art lock-free priority queues, both with relaxed and non-relaxed semantics, showing high performance and good scalability of our approach.

Connected through hyperedge HE-1 (induced by source paper "relaxed scheduling for scalable belief propagation"):
  with members:
    - "message scheduling for performant many core belief propagation"
    - "relaxed schedulers can efficiently parallelize iterative algorithms"
    - "theoretically efficient parallel graph algorithms can be fast and scalable"
    - "distributionally linearizable data structures"
    - "parallelism in randomized incremental algorithms"
    - "the power of choice in priority scheduling"
    - "greedy sequential maximal independent set and matching are parallel on average"
    - "residual belief propagation informed scheduling for asynchronous message passing"

Connected through hyperedge HE-2 (induced by source paper "using skip graphs for increased numa locality"):
  with members:
    - "global local view scalable consistency for concurrent data types"
    - "the adaptive priority queue with elimination and combining"
    - "flat combining and the synchronization parallelism tradeoff"

Connected through hyperedge HE-3 (induced by source paper "quantifiability concurrent correctness from first principles"):
  with members:
    - "specifying concurrent programs in separation logic morphisms and simulations"
    - "order out of chaos proving linearizability using local views"
    - "distributionally linearizable data structures"
    - "proving linearizability using forward simulations"
    - "benchmarking concurrent priority queues performance of k lsm and related data structures"
    - "a dynamic elimination combining stack algorithm"

Connected through hyperedge HE-4 (induced by source paper "monotonically relaxing concurrent data structure semantics for performance an efficient 2d design framework"):
  with members:
    - "the power of choice in priority scheduling"
    - "a dynamic elimination combining stack algorithm"
    - "flat combining and the synchronization parallelism tradeoff"

Connected through hyperedge HE-5 (induced by source paper "layering data structures over skip graphs for increased numa locality"):
  with members:
    - "global local view scalable consistency for concurrent data types"
    - "the adaptive priority queue with elimination and combining"

Connected through hyperedge HE-6 (induced by source paper "the power of choice in priority scheduling"):
  with members:
    - "a general technique for non blocking trees"

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.DS(Data Structures and Algorithms)