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 cost effective framework for preference elicitation and aggregation"
  Abstract: We propose a cost-effective framework for preference elicitation and aggregation under the Plackett-Luce model with features. Given a budget, our framework iteratively computes the most cost-effective elicitation questions in order to help the agents make a better group decision. #R##N#We illustrate the viability of the framework with experiments on Amazon Mechanical Turk, which we use to estimate the cost of answering different types of elicitation questions. We compare the prediction accuracy of our framework when adopting various information criteria that evaluate the expected information gain from a question. Our experiments show carefully designed information criteria are much more efficient, i.e., they arrive at the correct answer using fewer queries, than randomly asking questions given the budget constraint.

Connected through hyperedge HE-1 (induced by source paper "approximating voting rules from truncated ballots"):
  with members:
    - "comparing election methods where each voter ranks only few candidates"
    - "the computational impact of partial votes on strategic voting"

Connected through hyperedge HE-2 (induced by source paper "learning mixtures of plackett luce models from structured partial orders"):
  with members:
    - "learning mixtures of plackett luce models"
    - "top k ranking from pairwise comparisons when spectral ranking is optimal"
    - "data driven rank breaking for efficient rank aggregation"
    - "efficient probabilistic inference with partial ranking queries"
    - "active ranking using pairwise comparisons"
    - "restricted strong convexity and weighted matrix completion optimal bounds with noise"
    - "matrix completion from noisy entries"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-3 (induced by source paper "gradient based optimization for bayesian preference elicitation"):
  with members:
    - "constructive preference elicitation over hybrid combinatorial spaces"
    - "adam a method for stochastic optimization"
    - "ucp networks a directed graphical representation of conditional utilities"
    - "local utility elicitation in gai models"

Connected through hyperedge HE-4 (induced by source paper "composite marginal likelihood methods for random utility models"):
  with members:
    - "computational and statistical tradeoffs in learning to rank"
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "preference elicitation for general random utility models"
    - "random utility theory for social choice"

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)