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: "fault tolerant entity resolution with the crowd"
  Abstract: In recent years, crowdsourcing is increasingly applied as a means to enhance data quality. Although the crowd generates insightful information especially for complex problems such as entity resolution (ER), the output quality of crowd workers is often noisy. That is, workers may unintentionally generate false or contradicting data even for simple tasks. The challenge that we address in this paper is how to minimize the cost for task requesters while maximizing ER result quality under the assumption of unreliable input from the crowd. For that purpose, we first establish how to deduce a consistent ER solution from noisy worker answers as part of the data interpretation problem. We then focus on the next-crowdsource problem which is to find the next task that maximizes the information gain of the ER result for the minimal additional cost. We compare our robust data interpretation strategies to alternative state-of-the-art approaches that do not incorporate the notion of fault-tolerance, i.e., the robustness to noise. In our experimental evaluation we show that our approaches yield a quality improvement of at least 20% for two real-world datasets. Furthermore, we examine task-to-worker assignment strategies as well as task parallelization techniques in terms of their cost and quality trade-offs in this paper. Based on both synthetic and crowdsourced datasets, we then draw conclusions on how to minimize cost while maintaining high quality ER results.

Connected through hyperedge HE-1 (induced by source paper "correlation clustering with same cluster queries bounded by optimal cost"):
  with members:
    - "semi supervised algorithms for approximately optimal and accurate clustering"
    - "semi supervised clustering for de duplication"
    - "approximate correlation clustering using same cluster queries"
    - "approximate clustering with same cluster queries"
    - "clustering with same cluster queries"
    - "correlation clustering with a fixed number of clusters"

Connected through hyperedge HE-2 (induced by source paper "the schulze method of voting"):
  with members:
    - "approval based elections and distortion of voting rules"
    - "a note on the complexity of manipulating weighted schulze voting"
    - "the expanding approvals rule improving proportional representation and monotonicity"
    - "the flow network method"
    - "metric distortion of social choice rules lower bounds and fairness properties"
    - "choosing by means of approval preferential voting the revised approval choice"
    - "how hard is it to control an election by breaking ties"
    - "schulze and ranked pairs voting are fixed parameter tractable to bribe manipulate and control"

Connected through hyperedge HE-3 (induced by source paper "clustering via crowdsourcing"):
  with members:
    - "parallel algorithms for select and partition with noisy comparisons"
    - "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery"
    - "on reverse pinsker inequalities"
    - "parallel correlation clustering on big graphs"
    - "exact recovery in the stochastic block model"
    - "crowder crowdsourcing entity resolution"
    - "sorting searching and simulation in the mapreduce framework"
    - "fast clustering using mapreduce"

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)