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: "online packet scheduling under adversarial jamming"
  Abstract: We consider the problem of scheduling packets of different lengths via a directed communication link prone to jamming errors. Dynamic packet arrivals and errors are modelled by an adversary. We focus on estimating relative throughput of online scheduling algorithms, that is, the ratio between the throughputs achieved by the algorithm and the best scheduling for the same arrival and error patterns. This framework allows more accurate analysis of performance of online scheduling algorithms, even in worst-case arrival and error scenarios. We design an online algorithm for scheduling packets of arbitrary lengths, achieving optimal relative throughput in the range (1/3,1/2] (the exact value depends on packet lengths). In other words, for any arrival and jamming patterns, our solution gives throughput which is no more than c times worse than the best possible scheduling for these patters, where c in [2; 3) is the inverse of relative throughput. Another algorithm we design makes use of additional resources in order to achieve relative throughput 1, that is, it achieves at least as high throughput as the best schedule without such resources, for any arrival and jamming patterns. More precisely, we show that if the algorithm can run with double speed, i.e., with twice higher frequency, then its relative throughput is 1. This demonstrates that throughput of the best online scheduling algorithms scales well with resource augmentation. #R##N#Keywords: Packet scheduling, Dynamic packet arrivals, Adversarial jamming, Online algorithms, Relative throughput, Resource augmentation.

Connected through hyperedge HE-1 (induced by source paper "on packet scheduling with adversarial jamming and speedup"):
  with members:
    - "measuring the impact of adversarial errors on packet scheduling strategies"
    - "online parallel scheduling of non uniform tasks trading failures for energy"

Connected through hyperedge HE-2 (induced by source paper "measuring the impact of adversarial errors on packet scheduling strategies"):
  with members:
    - "dynamic packet scheduling in wireless networks"
    - "fifo queueing policies for packets with heterogeneous processing"

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)