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: "sequential multi class labeling in crowdsourcing"
  Abstract: We consider a crowdsourcing platform where workers' responses to questions posed by a crowdsourcer are used to determine the hidden state of a multi-class labeling problem. As workers may be unreliable, we propose to perform sequential questioning in which the questions posed to the workers are designed based on previous questions and answers. We propose a Partially-Observable Markov Decision Process (POMDP) framework to determine the best questioning strategy, subject to the crowdsourcer's budget constraint. As this POMDP formulation is in general intractable, we develop a suboptimal approach based on a $q$-ary Ulam-R\'enyi game. We also propose a sampling heuristic, which can be used in tandem with standard POMDP solvers, using our Ulam-R\'enyi strategy. We demonstrate through simulations that our approaches outperform a non-sequential strategy based on error correction coding and which does not utilize workers' previous responses.

Connected through hyperedge HE-1 (induced by source paper "error correcting neural network"):
  with members:
    - "a mean field theory of batch normalization"
    - "improving adversarial robustness via promoting ensemble diversity"
    - "batch normalization is a cause of adversarial vulnerability"
    - "adversarial attacks and defences competition"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "magnet a two pronged defense against adversarial examples"
    - "defense against adversarial attacks using high level representation guided denoiser"
    - "globally normalized transition based neural networks"

Connected through hyperedge HE-2 (induced by source paper "an unsupervised bayesian neural network for truth discovery"):
  with members:
    - "robust decentralized detection and social learning in tandem networks"
    - "whose opinion to follow in multihypothesis social learning a large deviations perspective"
    - "a survey on truth discovery"
    - "generalized denoising auto encoders as generative models"
    - "a bayesian approach to discovering truth from conflicting sources for data integration"

Connected through hyperedge HE-3 (induced by source paper "learning orthogonal projections in linear bandits"):
  with members:
    - "task recommendation in crowdsourcing based on learning preferences and reliabilities"
    - "context aware hierarchical online learning for performance maximization in mobile crowdsourcing"
    - "algorithms for multi armed bandit problems"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-4 (induced by source paper "task recommendation in crowdsourcing based on learning preferences and reliabilities"):
  with members:
    - "fair task allocation in crowdsourced delivery"
    - "risk averse multi armed bandit problems under mean variance measure"
    - "reliable crowdsourcing under the generalized dawid skene model"
    - "regularized minimax conditional entropy for crowdsourcing"
    - "risk aversion in multi armed bandits"
    - "budget optimal task allocation for reliable crowdsourcing systems"

Connected through hyperedge HE-5 (induced by source paper "using social network information in bayesian truth discovery"):
  with members:
    - "robust decentralized detection and social learning in tandem networks"
    - "whose opinion to follow in multihypothesis social learning a large deviations perspective"
    - "a survey on truth discovery"
    - "a bayesian approach to discovering truth from conflicting sources for data integration"

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.HC(Human-Computer Interaction)