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: "achieving budget optimality with adaptive schemes in crowdsourcing"
  Abstract: Crowdsourcing platforms provide marketplaces where task requesters can pay to get labels on their data. Such markets have emerged recently as popular venues for collecting annotations that are crucial in training machine learning models in various applications. However, as jobs are tedious and payments are low, errors are common in such crowdsourced labels. A common strategy to overcome such noise in the answers is to add redundancy by getting multiple answers for each task and aggregating them using some methods such as majority voting. For such a system, there is a fundamental question of interest: how can we maximize the accuracy given a fixed budget on how many responses we can collect on the crowdsourcing system. We characterize this fundamental trade-off between the budget (how many answers the requester can collect in total) and the accuracy in the estimated labels. In particular, we ask whether adaptive task assignment schemes lead to a more efficient trade-off between the accuracy and the budget. #R##N#Adaptive schemes, where tasks are assigned adaptively based on the data collected thus far, are widely used in practical crowdsourcing systems to efficiently use a given fixed budget. However, existing theoretical analyses of crowdsourcing systems suggest that the gain of adaptive task assignments is minimal. To bridge this gap, we investigate this question under a strictly more general probabilistic model, which has been recently introduced to model practical crowdsourced annotations. Under this generalized Dawid-Skene model, we characterize the fundamental trade-off between budget and accuracy. We introduce a novel adaptive scheme that matches this fundamental limit. We further quantify the fundamental gap between adaptive and non-adaptive schemes, by comparing the trade-off with the one for non-adaptive schemes. Our analyses confirm that the gap is significant.

Connected through hyperedge HE-1 (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"
    - "an information theoretic framework for designing information elicitation mechanisms that reward truth telling"
    - "a permutation based model for crowd labeling optimal estimation and robustness"
    - "budget optimal task allocation for reliable crowdsourcing systems"

Connected through hyperedge HE-2 (induced by source paper "buoca budget optimized crowd worker allocation"):
  with members:
    - "predicting foreground object ambiguity and efficiently crowdsourcing the segmentation s"
    - "statistical decision making for optimal budget allocation in crowd labeling"
    - "budget optimal task allocation for reliable crowdsourcing systems"

Connected through hyperedge HE-3 (induced by source paper "budget aware online task assignment in spatial crowdsourcing"):
  with members:
    - "prediction based task assignment in spatial crowdsourcing"
    - "task assignment on multi skill oriented spatial crowdsourcing"
    - "fundamental limits of budget fidelity trade off in label crowdsourcing"
    - "reliable diversity based spatial crowdsourcing by moving workers"
    - "budget optimal task allocation for reliable crowdsourcing systems"

Connected through hyperedge HE-4 (induced by source paper "iterative bayesian learning for crowdsourced regression"):
  with members:
    - "ssd single shot multibox detector"
    - "a permutation based model for crowd labeling optimal estimation and robustness"
    - "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 "sequential multi class labeling in crowdsourcing"):
  with members:
    - "regularized minimax conditional entropy for crowdsourcing"
    - "near optimally teaching the crowd to classify"
    - "reliable crowdsourcing for multi class labeling using coding theory"
    - "budget optimal task allocation for reliable crowdsourcing systems"
    - "online planning algorithms for pomdps"

Connected through hyperedge HE-6 (induced by source paper "optimality of belief propagation for crowdsourced classification"):
  with members:
    - "regularized minimax conditional entropy for crowdsourcing"
    - "spectral detection in the censored block model"
    - "max product belief propagation for linear programming applications to combinatorial optimization"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "budget optimal task allocation for reliable crowdsourcing systems"
    - "matrix completion from a few entries"

Connected through hyperedge HE-7 (induced by source paper "selecting the top quality item through crowd scoring"):
  with members:
    - "relative upper confidence bound for the k armed dueling bandit 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.LG(Machine Learning)