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 ranking models"
  Abstract: This work concerns learning probabilistic models for ranking data in a heterogeneous population. The specific problem we study is learning the parameters of a Mallows Mixture Model. Despite being widely studied, current heuristics for this problem do not have theoretical guarantees and can get stuck in bad local optima. We present the first polynomial time algorithm which provably learns the parameters of a mixture of two Mallows models. A key component of our algorithm is a novel use of tensor decomposition techniques to learn the top-k prefix in both the rankings. Before this work, even the question of identifiability in the case of a mixture of two Mallows models was unresolved.

Connected through hyperedge HE-1 (induced by source paper "supporting hard queries over probabilistic preferences"):
  with members:
    - "learning mixed membership mallows models from pairwise comparisons"
    - "riffled independence for efficient inference with partial rankings"

Connected through hyperedge HE-2 (induced by source paper "optimal learning of mallows block model"):
  with members:
    - "efficiently learning mixtures of mallows models"
    - "consensus ranking under the exponential model"
    - "content modeling using latent permutations"
    - "statistical exponential families a digest with flash cards"
    - "sorting from noisy information"

Connected through hyperedge HE-3 (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 plackett luce models"
    - "preference elicitation for general random utility models"
    - "noisy sorting without resampling"
    - "vote elicitation complexity and strategy proofness"

Connected through hyperedge HE-4 (induced by source paper "efficiently learning mixtures of mallows models"):
  with members:
    - "learning mixtures of plackett luce models"
    - "statistical query lower bounds for robust estimation of high dimensional gaussians and gaussian mixtures"
    - "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-5 (induced by source paper "inhomogoenous hypergraph clustering with applications"):
  with members:
    - "on spectral partitioning of signed graphs"
    - "higher order organization of complex networks"
    - "estimation of kl divergence optimal minimax rate"
    - "scalable motif aware graph clustering"
    - "motif clustering and overlapping clustering for social network analysis"
    - "hypergraph markov operators eigenvalues and approximation algorithms"
    - "maximum likelihood estimation of functionals of discrete distributions"
    - "on the approximation of submodular functions"

Connected through hyperedge HE-6 (induced by source paper "assortment optimisation under a general discrete choice model a tight analysis of revenue ordered assortments"):
  with members:
    - "assortment optimization under a multinomial logit model with position bias and social influence"
    - "the stackelberg minimum spanning tree game"

Connected through hyperedge HE-7 (induced by source paper "learning mixtures of plackett luce models"):
  with members:
    - "adversarial top k ranking"

Connected through hyperedge HE-8 (induced by source paper "spectral mle top k rank aggregation from pairwise comparisons"):
  with members:
    - "information recovery from pairwise measurements a shannon theoretic approach"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "renyi divergence and kullback leibler divergence"
    - "a statistical decision theoretic framework for social choice"
    - "multiple identifications in multi armed bandits"
    - "active ranking using pairwise comparisons"
    - "an active learning algorithm for ranking from pairwise preferences with an almost optimal query complexity"

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)