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: "regularized minimax conditional entropy for crowdsourcing"
  Abstract: There is a rapidly increasing interest in crowdsourcing for data labeling. By crowdsourcing, a large number of labels can be often quickly gathered at low cost. However, the labels provided by the crowdsourcing workers are usually not of high quality. In this paper, we propose a minimax conditional entropy principle to infer ground truth from noisy crowdsourced labels. Under this principle, we derive a unique probabilistic labeling model jointly parameterized by worker ability and item difficulty. We also propose an objective measurement principle, and show that our method is the only method which satisfies this objective measurement principle. We validate our method through a variety of real crowdsourcing datasets with binary, multiclass or ordinal labels.

Connected through hyperedge HE-1 (induced by source paper "collaborative data acquisition"):
  with members:
    - "selling multiple items via social networks"
    - "multi winner contests for strategic diffusion in social networks"
    - "mechanism design in social networks"
    - "time critical social mobilization"
    - "optimal crowdsourcing contests"

Connected through hyperedge HE-2 (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"
    - "a permutation based model for crowd labeling optimal estimation and robustness"
    - "doubly robust off policy value evaluation for reinforcement learning"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-3 (induced by source paper "practice of efficient data collection via crowdsourcing at large scale"):
  with members:
    - "approval voting and incentives in crowdsourcing"
    - "incentivizing high quality crowdwork"
    - "how many workers to ask adaptive exploration for collecting high quality labels"
    - "truelabel confusions a spectrum of probabilistic models in analyzing multiple ratings"
    - "learning to predict the wisdom of crowds"

Connected through hyperedge HE-4 (induced by source paper "latent distribution assumption for unbiased and consistent consensus modelling"):
  with members:
    - "label distribution learning"
    - "how to grade a test without knowing the answers a bayesian graphical model for adaptive crowdsourcing and aptitude testing"

Connected through hyperedge HE-5 (induced by source paper "crowdsourcing data acquisition via social networks"):
  with members:
    - "time critical social mobilization"
    - "optimal crowdsourcing contests"

Connected through hyperedge HE-6 (induced by source paper "aggregation of pairwise comparisons with reduction of biases"):
  with members:
    - "simple robust and optimal ranking from pairwise comparisons"

Connected through hyperedge HE-7 (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"
    - "optimality of belief propagation for crowdsourced classification"
    - "putting peer prediction under the micro economic scope and making truth telling focal"
    - "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-8 (induced by source paper "task recommendation in crowdsourcing based on learning preferences and reliabilities"):
  with members:
    - "fair task allocation in crowdsourced delivery"
    - "sequential multi class labeling in crowdsourcing"
    - "risk averse multi armed bandit problems under mean variance measure"
    - "reliable crowdsourcing under the generalized dawid skene model"
    - "risk aversion in multi armed bandits"
    - "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)