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: "learning mixtures of plackett luce models"
  Abstract: In this paper we address the identifiability and efficient learning problems of finite mixtures of Plackett-Luce models for rank data. We prove that for any $k\geq 2$, the mixture of $k$ Plackett-Luce models for no more than $2k-1$ alternatives is non-identifiable and this bound is tight for $k=2$. For generic identifiability, we prove that the mixture of $k$ Plackett-Luce models over $m$ alternatives is generically identifiable if $k\leq\lfloor\frac {m-2} 2\rfloor!$. We also propose an efficient generalized method of moments (GMM) algorithm to learn the mixture of two Plackett-Luce models and show that the algorithm is consistent. Our experiments show that our GMM algorithm is significantly faster than the EMM algorithm by Gormley and Murphy (2008), while achieving competitive statistical efficiency.

Connected through hyperedge HE-1 (induced by source paper "choice set optimization under discrete choice models of group decisions"):
  with members:
    - "learning choice functions"
    - "learning mixtures of plackett luce models from structured partial orders"
    - "discovering context effects from raw choice data"
    - "on the convergence of adam and beyond"
    - "learning choice functions concepts and architectures"
    - "pytorch an imperative style high performance deep learning library"
    - "predicting choice with set dependent aggregation"
    - "reasoning in bayesian opinion exchange networks is pspace hard"

Connected through hyperedge HE-2 (induced by source paper "who is in your top three optimizing learning in elections with many candidates"):
  with members:
    - "truthful aggregation of budget proposals"
    - "a framework for approval based budgeting methods"
    - "designing optimal binary rating systems"
    - "a quantitative analysis of multi winner rules"
    - "designing informative rating systems for online platforms evidence from two experiments"
    - "consistent approval based multi winner rules"
    - "iterative local voting for collective decision making in continuous spaces"
    - "optimizing positional scoring rules for rank aggregation"

Connected through hyperedge HE-3 (induced by source paper "learning mixtures of plackett luce models from structured partial orders"):
  with members:
    - "a cost effective framework for preference elicitation and aggregation"
    - "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-4 (induced by source paper "minimizing time to rank a learning and recommendation approach"):
  with members:
    - "composite marginal likelihood methods for random utility models"
    - "learning mixtures of ranking models"
    - "preference elicitation for general random utility models"
    - "noisy sorting without resampling"
    - "vote elicitation complexity and strategy proofness"

Connected through hyperedge HE-5 (induced by source paper "efficiently learning mixtures of mallows models"):
  with members:
    - "statistical query lower bounds for robust estimation of high dimensional gaussians and gaussian mixtures"
    - "learning mixtures of ranking models"
    - "dirichlet process mixtures of generalized mallows models"
    - "tensor decompositions for learning latent variable models"
    - "settling the polynomial learnability of mixtures of gaussians"
    - "polynomial learning of distribution families"
    - "noisy sorting without resampling"

Connected through hyperedge HE-6 (induced by source paper "entity set search of scientific literature an unsupervised ranking approach"):
  with members:
    - "pytrec_eval an extremely fast python interface to trec_eval"
    - "on type aware entity retrieval"
    - "word entity duet representations for document ranking"

Connected through hyperedge HE-7 (induced by source paper "towards non parametric learning to rank"):
  with members:
    - "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)