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: "election with bribed voter uncertainty hardness and approximation algorithm"
  Abstract: Bribery in election (or computational social choice in general) is an important problem that has received a considerable amount of attention. In the classic bribery problem, the briber (or attacker) bribes some voters in attempting to make the briber's designated candidate win an election. In this paper, we introduce a novel variant of the bribery problem, "Election with Bribed Voter Uncertainty" or BVU for short, accommodating the uncertainty that the vote of a bribed voter may or may not be counted. This uncertainty occurs either because a bribed voter may not cast its vote in fear of being caught, or because a bribed voter is indeed caught and therefore its vote is discarded. As a first step towards ultimately understanding and addressing this important problem, we show that it does not admit any multiplicative $O(1)$-approximation algorithm modulo standard complexity assumptions. We further show that there is an approximation algorithm that returns a solution with an additive-$\epsilon$ error in FPT time for any fixed $\epsilon$.

Connected through hyperedge HE-1 (induced by source paper "election control with voters uncertainty hardness and approximation results"):
  with members:
    - "vote for me election control via social influence in arbitrary scoring rule voting systems"
    - "election campaigning on social media politicians audiences and the mediation of political communication on facebook and twitter"
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"
    - "controlling elections through social influence"
    - "almost polynomial ratio eth hardness of approximating densest k subgraph"
    - "complexity of shift bribery in committee elections"
    - "minority becomes majority in social networks"
    - "determining possible and necessary winners under common voting rules given partial orders"

Connected through hyperedge HE-2 (induced by source paper "person job fit adapting the right talent for the right job with joint representation learning"):
  with members:
    - "enhancing person job fit for talent recruitment an ability aware neural network approach"
    - "collaborative company profiling insights from an employee s perspective"
    - "measuring the popularity of job skills in recruitment market a multi criteria approach"
    - "comparative study of cnn and rnn for natural language processing"
    - "recruitment market trend analysis with sequential latent variable models"
    - "combining recurrent and convolutional neural networks for relation classification"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

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)