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: "feasibility analysis in the sporadic dag task model"
  Abstract: Real-time systems increasingly contain processing units with multiple cores. To use this additional computational power in hard deadline environments, one needs schedulability tests for task models that represent the possibilities of parallel execution of jobs of a task. A standard model is to represent a (sporadically) recurrent task by a directed a cyclic graph (DAG). The nodes of the DAG correspond to the jobs of the task. All such jobs are released simultaneously, have to be completed within some common relative deadline, and some pairs of jobs are linked by a precedence constraint, i.e., an arc of the DAG. This poses new challenges for analyzing whether a task system is feasible, in particular for the commonly used online algorithms Earliest Deadline First (EDF) and Deadline Monotonic (DM). While for ordinary sporadic tasks the required algorithmic techniques are well-understood, despite recent research much remains open in this model. In this work, we completely close the gap between the algorithmic understanding of feasibility analysis for the usual sporadic task model and the case where each sporadic task is a DAG. We show for DAG tasks that EDF has a tight speedup bound of 2 - 1/m, where m is the number of processors, while DM has a speedup bound of at most 3 - 1/m. Moreover, we present polynomial and pseudopolynomial time tests, of differing effectiveness, for determining whether a set of sporadic DAG tasks can be scheduled by EDF or DM to meet all deadlines on a specified number of processors. We remark that the effectiveness of some of our tests matches the best known algorithms for ordinary sporadic task sets, thus closing the gap.

Connected through hyperedge HE-1 (induced by source paper "cache where you want reconciling predictability and coherent caching"):
  with members:
    - "hourglass predictable time based cache coherence protocol for dual critical multi core systems"
    - "verifying sequential consistency on shared memory multiprocessors by model checking"

Connected through hyperedge HE-2 (induced by source paper "new analysis techniques for supporting hard real time sporadic dag task systems on multiprocessors"):
  with members:
    - "semi federated scheduling of parallel real time tasks on multiprocessors"

Connected through hyperedge HE-3 (induced by source paper "reservation based federated scheduling for parallel real time tasks"):
  with members:
    - "push forward global fixed priority scheduling of arbitrary deadline sporadic task systems"
    - "semi federated scheduling of parallel real time tasks on multiprocessors"
    - "federated scheduling admits no constant speedup factors for constrained deadline dag task systems"
    - "partitioned multiprocessor fixed priority scheduling of sporadic real time tasks"

Connected through hyperedge HE-4 (induced by source paper "k2u a general framework from k point effective schedulability analysis to utilization based tests"):
  with members:
    - "evaluate and compare two uitlization based schedulability test frameworks for real time systems"
    - "partitioned multiprocessor fixed priority scheduling of sporadic real time tasks"
    - "k2q a quadratic form response time and schedulability analysis framework for utilization based analysis"

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)