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: "budget optimal task allocation for reliable crowdsourcing systems"
  Abstract: Crowdsourcing systems, in which numerous tasks are electronically distributed to numerous "information piece-workers", have emerged as an effective paradigm for human-powered solving of large scale problems in domains such as image classification, data entry, optical character recognition, recommendation, and proofreading. Because these low-paid workers can be unreliable, nearly all such systems must devise schemes to increase confidence in their answers, typically by assigning each task multiple times and combining the answers in an appropriate manner, e.g. majority voting. #R##N#In this paper, we consider a general model of such crowdsourcing tasks and pose the problem of minimizing the total price (i.e., number of task assignments) that must be paid to achieve a target overall reliability. We give a new algorithm for deciding which tasks to assign to which workers and for inferring correct answers from the workers' answers. We show that our algorithm, inspired by belief propagation and low-rank matrix approximation, significantly outperforms majority voting and, in fact, is optimal through comparison to an oracle that knows the reliability of every worker. Further, we compare our approach with a more general class of algorithms which can dynamically assign tasks. By adaptively deciding which questions to ask to the next arriving worker, one might hope to reduce uncertainty more efficiently. We show that, perhaps surprisingly, the minimum price necessary to achieve a target reliability scales in the same manner under both adaptive and non-adaptive scenarios. Hence, our non-adaptive approach is order-optimal under both scenarios. This strongly relies on the fact that workers are fleeting and can not be exploited. Therefore, architecturally, our results suggest that building a reliable worker-reputation system is essential to fully harnessing the potential of adaptive designs.

Connected through hyperedge HE-1 (induced by source paper "crowdsourced classification with xor queries fundamental limits and an efficient algorithm"):
  with members:
    - "community recovery in hypergraphs"
    - "iterative bayesian learning for crowdsourced regression"
    - "optimality of belief propagation for crowdsourced classification"
    - "noisy tensor completion via the sum of squares hierarchy"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-2 (induced by source paper "online policies for efficient volunteer crowdsourcing"):
  with members:
    - "allocation problems in ride sharing platforms online matching with offline reusable resources"
    - "guaranteed non convex optimization submodular maximization over continuous domains"
    - "bayesian combinatorial auctions expanding single buyer mechanisms to many buyers"
    - "online stochastic matching beating 1 1 e"

Connected through hyperedge HE-3 (induced by source paper "online allocation of reusable resources achieving optimal competitive ratio"):
  with members:
    - "online matching with stochastic rewards optimal competitive ratio via path based formulation"
    - "algorithms for online matching assortment and pricing with tight weight dependent competitive ratios"
    - "allocation problems in ride sharing platforms online matching with offline reusable resources"
    - "online stochastic matching beating 1 1 e"

Connected through hyperedge HE-4 (induced by source paper "online matching with stochastic rewards optimal competitive ratio via path based formulation"):
  with members:
    - "online allocation of reusable resources achieving optimal competitive ratio"
    - "online primal dual meets online matching with stochastic rewards configuration lp to the rescue"
    - "vertex weighted online stochastic matching with patience constraints"
    - "new algorithms better bounds and a novel model for online stochastic matching"
    - "when lp is the cure for your matching woes improved bounds for stochastic matchings"
    - "online stochastic matching online actions based on offline statistics"
    - "online stochastic matching beating 1 1 e"
    - "algorithms for secretary problems on graphs and hypergraphs"

Connected through hyperedge HE-5 (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"
    - "a permutation based model for crowd labeling optimal estimation and robustness"

Connected through hyperedge HE-6 (induced by source paper "accurate inference of crowdsourcing properties when using efficient allocation strategies"):
  with members:
    - "efficient crowd exploration of large networks the case of causal attribution"
    - "reply supply efficient crowdsourcing when workers do more than answer questions"
    - "crowdsourcing predictors of residential electric energy usage"
    - "crowdsourcing predictors of behavioral outcomes"

Connected through hyperedge HE-7 (induced by source paper "crowdsourcing via pairwise co occurrences identifiability and algorithms"):
  with members:
    - "on identifiability of nonnegative matrix factorization"
    - "learning hidden markov models from pairwise co occurrences with applications to topic modeling"
    - "a tensor approach to learning mixed membership community models"
    - "a practical algorithm for topic modeling with provable guarantees"

Connected through hyperedge HE-8 (induced by source paper "efficient crowdsourcing of crowd generated microtasks"):
  with members:
    - "efficient crowd exploration of large networks the case of causal attribution"
    - "reply supply efficient crowdsourcing when workers do more than answer questions"
    - "crowdsourcing predictors of residential electric energy usage"
    - "crowdsourcing predictors of behavioral outcomes"

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)