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: "paris predicting application resilience using machine learning"
  Abstract: Extreme-scale scientific applications can be more vulnerable to soft errors (transient faults) as high-performance computing systems increase in scale. The common practice to evaluate the resilience to faults of an application is random fault injection, a method that can be highly time consuming. While resilience prediction modeling has been recently proposed to predict application resilience in a faster way than fault injection, it can only predict a single class of fault manifestation (SDC) and there is no evidence demonstrating that it can work on previously unseen programs, which greatly limits its re-usability. We present PARIS, a resilience prediction method that addresses the problems of existing prediction methods using machine learning. Using carefully-selected features and a machine learning model, our method is able to make resilience predictions of three classes of fault manifestations (success, SDC, and interruption) as opposed to one class like in current resilience prediction modeling. The generality of our approach allows us to make prediction on new applications, i.e., previously unseen applications, providing large applicability to our model. Our evaluation on 125 programs shows that PARIS provides high prediction accuracy, 82% and 77% on average for predicting the rate of success and interruption, respectively, while the state-of-the-art resilience prediction model cannot predict them. When predicting the rate of SDC, PARIS provides much better accuracy than the state-of-the-art (38% vs. -273%). PARIS is much faster (up to 450x speedup) than the traditional method (random fault injection).

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)