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: "a permutation based model for crowd labeling optimal estimation and robustness"
  Abstract: The aggregation and denoising of crowd labeled data is a task that has gained increased significance with the advent of crowdsourcing platforms and massive datasets. In this paper, we propose a permutation-based model for crowd labeled data that is a significant generalization of the common Dawid-Skene model, and introduce a new error metric by which to compare different estimators. Working in a high-dimensional non-asymptotic framework that allows both the number of workers and tasks to scale, we derive optimal rates of convergence for the permutation-based model. We show that the permutation-based model offers significant robustness in estimation due to its richness, while surprisingly incurring only a small additional statistical penalty as compared to the Dawid-Skene model. Finally, we propose a computationally-efficient method, called the OBI-WAN estimator, that is uniformly optimal over a class intermediate between the permutation-based and the Dawid-Skene models, and is uniformly consistent over the entire permutation-based model class. In contrast, the guarantees for estimators available in prior literature are sub-optimal over the original Dawid-Skene model.

Connected through hyperedge HE-1 (induced by source paper "doubly robust crowdsourcing"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "learning from noisy singly labeled data"
    - "iterative bayesian learning for crowdsourced regression"
    - "doubly robust off policy value evaluation for reinforcement learning"
    - "regularized minimax conditional entropy for crowdsourcing"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-2 (induced by source paper "max mig an information theoretic approach for joint learning from crowds"):
  with members:
    - "water from two rocks maximizing the mutual information"
    - "learning from noisy singly labeled data"
    - "who said what modeling individual labelers improves classification"
    - "achieving budget optimality with adaptive schemes in crowdsourcing"
    - "an information theoretic framework for designing information elicitation mechanisms that reward truth telling"
    - "budget optimal task allocation for reliable crowdsourcing systems"

Connected through hyperedge HE-3 (induced by source paper "worst case vs average case design for estimation from fixed pairwise comparisons"):
  with members:
    - "active ranking from pairwise comparisons and when parametric assumptions don t help"
    - "competitive analysis of the top k ranking problem"
    - "feeling the bern adaptive estimators for bernoulli probabilities of pairwise comparisons"
    - "top k ranking from pairwise comparisons when spectral ranking is optimal"
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "estimation from pairwise comparisons sharp minimax bounds with topology dependence"
    - "simple robust and optimal ranking from pairwise comparisons"
    - "active ranking using pairwise comparisons"

Connected through hyperedge HE-4 (induced by source paper "iterative bayesian learning for crowdsourced regression"):
  with members:
    - "achieving budget optimality with adaptive schemes in crowdsourcing"
    - "ssd single shot multibox detector"
    - "regularized minimax conditional entropy for crowdsourcing"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"
    - "tuned models of peer assessment in moocs"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"

Connected through hyperedge HE-5 (induced by source paper "reliable crowdsourcing under the generalized dawid skene model"):
  with members:
    - "crowdsourcing with sparsely interacting workers"
    - "embeddings of schatten norms with applications to data streams"
    - "spectrum estimation from samples"
    - "regularized minimax conditional entropy for crowdsourcing"
    - "spectral redemption in clustering sparse networks"
    - "budget optimal task allocation for reliable crowdsourcing systems"
    - "matrix completion from a few entries"

Connected through hyperedge HE-6 (induced by source paper "achieving budget optimality with adaptive schemes in crowdsourcing"):
  with members:
    - "optimality of belief propagation for crowdsourced classification"
    - "regularized minimax conditional entropy for crowdsourcing"
    - "budget optimal task allocation for reliable crowdsourcing systems"

Connected through hyperedge HE-7 (induced by source paper "universal clustering via crowdsourcing"):
  with members:
    - "multi object classification via crowdsourcing with a reject option"
    - "reliable crowdsourcing for multi class labeling using coding theory"
    - "budget optimal task allocation for reliable crowdsourcing systems"

Connected through hyperedge HE-8 (induced by source paper "double or nothing multiplicative incentive mechanisms for crowdsourcing"):
  with members:
    - "competitive analysis of the top k ranking problem"
    - "reliable crowdsourcing under the generalized dawid skene model"
    - "regularized minimax conditional entropy for crowdsourcing"
    - "estimation from pairwise comparisons sharp minimax bounds with topology dependence"
    - "approval voting and incentives in crowdsourcing"
    - "simple robust and optimal ranking from pairwise comparisons"
    - "mechanism design for crowdsourcing an optimal 1 1 e competitive budget feasible mechanism for large markets"
    - "reliable crowdsourcing for multi class labeling using coding theory"

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.LG(Machine Learning)