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: "select your questions wisely for entity resolution with crowd errors"
  Abstract: Crowdsourcing is becoming increasingly important in entity resolution tasks due to their inherent complexity such as clustering of images and natural language processing. Humans can provide more insightful information for these difficult problems compared to machine-based automatic techniques. Nevertheless, human workers can make mistakes due to lack of domain expertise or seriousness, ambiguity, or even due to malicious intents. The state-of-the-art literature usually deals with human errors via majority voting or by assigning a universal error rate over crowd workers. However, such approaches are incomplete, and often inconsistent, because the expertise of crowd workers are diverse with possible biases, thereby making it largely inappropriate to assume a universal error rate for all workers over all crowdsourcing tasks. #R##N#To this end, we mitigate the above challenges by considering an uncertain graph model, where the edge probability between two records A and B denotes the ratio of crowd workers who voted Yes on the question if A and B are same entity. In order to reflect independence across different crowdsourcing tasks, we apply the well-established notion of possible worlds, and develop parameter-free algorithms both for next crowdsourcing, as well as for entity resolution problems. In particular, using our framework, the problem of entity resolution becomes equivalent to finding the maximum-likelihood clustering; whereas for the next crowdsourcing, we identify the record pair that maximally increases the reliability of the maximum-likelihood clustering. Based on detailed empirical analysis over real-world datasets, we find that our proposed solution, PERC (probabilistic entity resolution with imperfect crowd) improves the quality by 15% and reduces the overall cost by 50% for the crowdsourcing-based entity resolution problem.

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.DB(Databases)