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: "optimality of belief propagation for crowdsourced classification"
  Abstract: Crowdsourcing systems are popular for solving large-scale labelling tasks with low-paid (or even non-paid) workers. We study the problem of recovering the true labels from the possibly erroneous crowdsourced labels under the popular Dawid-Skene model. To address this inference problem, several algorithms have recently been proposed, but the best known guarantee is still significantly larger than the fundamental limit. We close this gap under a simple but canonical scenario where each worker is assigned at most two tasks. In particular, we introduce a tighter lower bound on the fundamental limit and prove that Belief Propagation (BP) exactly matches this lower bound. The guaranteed optimality of BP is the strongest in the sense that it is information-theoretically impossible for any other algorithm to correctly label a larger fraction of the tasks. In the general setting, when more than two tasks are assigned to each worker, we establish the dominance result on BP that it outperforms all existing algorithms with provable guarantees. Experimental results suggest that BP is close to optimal for all regimes considered, while all other algorithms show suboptimal performances in certain regimes.

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"
    - "noisy tensor completion via the sum of squares hierarchy"
    - "imagenet large scale visual recognition challenge"
    - "budget optimal task allocation for reliable crowdsourcing systems"

Connected through hyperedge HE-2 (induced by source paper "combining crowd and machines for multi predicate item screening"):
  with members:
    - "crowd based multi predicate screening of papers in literature reviews"
    - "crowdsourcing paper screening in systematic literature reviews"
    - "crowdsourcing information extraction for biomedical systematic reviews"
    - "data fusion resolving conflicts from multiple sources"
    - "crowd access path optimization diversity matters"
    - "truelabel confusions a spectrum of probabilistic models in analyzing multiple ratings"
    - "crowder crowdsourcing entity resolution"

Connected through hyperedge HE-3 (induced by source paper "quality control in crowdsourcing a survey of quality attributes assessment techniques and assurance actions"):
  with members:
    - "crowd guilds worker led reputation and feedback on crowdsourcing platforms"
    - "a glimpse far into the future understanding long term crowd worker quality"
    - "embracing error to enable rapid crowdsourcing"
    - "crowd access path optimization diversity matters"
    - "incentivizing high quality crowdwork"
    - "quizz targeted crowdsourcing with a billion potential users"
    - "how many workers to ask adaptive exploration for collecting high quality labels"
    - "near optimally teaching the crowd to classify"

Connected through hyperedge HE-4 (induced by source paper "a technical survey on statistical modelling and design methods for crowdsourcing quality control"):
  with members:
    - "quality control in crowdsourcing a survey of quality attributes assessment techniques and assurance actions"
    - "peer prediction with heterogeneous tasks"
    - "putting peer prediction under the micro economic scope and making truth telling focal"
    - "regularized minimax conditional entropy for crowdsourcing"
    - "time sensitive bayesian information aggregation for crowdsourcing systems"
    - "approval voting and incentives in crowdsourcing"
    - "a survey on truth discovery"
    - "quizz targeted crowdsourcing with a billion potential users"

Connected through hyperedge HE-5 (induced by source paper "optimal inference in crowdsourced classification via belief propagation"):
  with members:
    - "regularized minimax conditional entropy for crowdsourcing"
    - "spectral detection on sparse hypergraphs"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "achieving budget optimality with adaptive schemes in crowdsourcing"):
  with members:
    - "a permutation based model for crowd labeling optimal estimation and robustness"
    - "regularized minimax conditional entropy for crowdsourcing"
    - "budget optimal task allocation for reliable crowdsourcing systems"

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)