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: "rank aggregation via nuclear norm minimization"
  Abstract: The process of rank aggregation is intimately intertwined with the structure of skew-symmetric matrices. We apply recent advances in the theory and algorithms of matrix completion to skew-symmetric matrices. This combination of ideas produces a new method for ranking a set of items. The essence of our idea is that a rank aggregation describes a partially filled skew-symmetric matrix. We extend an algorithm for matrix completion to handle skew-symmetric data and use that to extract ranks for each item. Our algorithm applies to both pairwise comparison and rating data. Because it is based on matrix completion, it is robust to both noise and incomplete data. We show a formal recovery result for the noiseless case and present a detailed study of the algorithm on synthetic data and Netflix ratings.

Connected through hyperedge HE-1 (induced by source paper "weighted matrix completion from non random non uniform sampling patterns"):
  with members:
    - "matrix completion with deterministic pattern a geometric perspective"
    - "on deterministic sampling patterns for robust low rank matrix completion"
    - "fundamental conditions for low cp rank tensor completion"
    - "rank determination for low rank data completion"
    - "matrix completion via max norm constrained optimization"
    - "weighted matrix completion and recovery with prior subspace information"
    - "recovery guarantee of weighted low rank approximation via alternating minimization"
    - "mc 2 a two phase algorithm for leveraged matrix completion"

Connected through hyperedge HE-2 (induced by source paper "the sparse reverse of principal component analysis for fast low rank matrix completion"):
  with members:
    - "fast low rank bayesian matrix completion with hierarchical gaussian prior models"
    - "guaranteed matrix completion via non convex factorization"
    - "online matrix completion and online robust pca"
    - "fast matrix completion without the condition number"
    - "strongly convex programming for exact matrix completion and robust principal component analysis"
    - "the power of convex relaxation near optimal matrix completion"
    - "robust principal component analysis"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-3 (induced by source paper "complementary attributes a new clue to zero shot learning"):
  with members:
    - "zero shot visual recognition using semantics preserving adversarial embedding networks"
    - "joint intermodal and intramodal label transfers for extremely rare or unseen classes"
    - "from zero shot learning to conventional supervised classification unseen visual data synthesis"
    - "dynamic routing between capsules"
    - "learning to compare relation network for few shot learning"
    - "recovering the missing link predicting class attribute associations for unsupervised zero shot learning"
    - "learning a deep embedding model for zero shot learning"
    - "label embedding for image classification"

Connected through hyperedge HE-4 (induced by source paper "active ranking with subset wise preferences"):
  with members:
    - "a nearly instance optimal algorithm for top k ranking under the multinomial logit model"
    - "assortment optimisation under a general discrete choice model a tight analysis of revenue ordered assortments"
    - "data driven rank breaking for efficient rank aggregation"
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "contextual dueling bandits"
    - "random utility theory for social choice"
    - "active ranking using pairwise comparisons"
    - "an active learning algorithm for ranking from pairwise preferences with an almost optimal query complexity"

Connected through hyperedge HE-5 (induced by source paper "how many pairwise preferences do we need to rank a graph consistently"):
  with members:
    - "inductive pairwise ranking going beyond the n log n barrier"
    - "birank towards ranking on bipartite graphs"
    - "active ranking using pairwise comparisons"
    - "an active learning algorithm for ranking from pairwise preferences with an almost optimal query complexity"
    - "noisy sorting without resampling"

Connected through hyperedge HE-6 (induced by source paper "savoias a diverse multi category visual complexity dataset"):
  with members:
    - "automatic understanding of image and video advertisements"
    - "detecting people in artwork with cnns"
    - "microsoft coco common objects in context"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-7 (induced by source paper "deconvolving feedback loops in recommender systems"):
  with members:
    - "from amateurs to connoisseurs modeling the evolution of user expertise through online reviews"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-8 (induced by source paper "hodge laplacians on graphs"):
  with members:
    - "geometric deep learning going beyond euclidean data"
    - "graph isomorphism in quasipolynomial time"
    - "algorithm 937 minres qlp for symmetric and hermitian linear equations and least squares problems"
    - "flows and decompositions of games harmonic and potential games"
    - "dynamics in near potential games"
    - "least squares ranking on graphs"
    - "an inverse power method for nonlinear eigenproblems with applications in 1 spectral clustering and sparse pca"

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.NA(Numerical Analysis)