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: "least squares ranking on graphs"
  Abstract: Given a set of alternatives to be ranked, and some pairwise comparison data, ranking is a least squares computation on a graph. The vertices are the alternatives, and the edge values comprise the comparison data. The basic idea is very simple and old: come up with values on vertices such that their differences match the given edge data. Since an exact match will usually be impossible, one settles for matching in a least squares sense. This formulation was first described by Leake in 1976 for rankingfootball teams and appears as an example in Professor Gilbert Strang's classic linear algebra textbook. If one is willing to look into the residual a little further, then the problem really comes alive, as shown effectively by the remarkable recent paper of Jiang et al. With or without this twist, the humble least squares problem on graphs has far-reaching connections with many current areas ofresearch. These connections are to theoretical computer science (spectral graph theory, and multilevel methods for graph Laplacian systems); numerical analysis (algebraic multigrid, and finite element exterior calculus); other mathematics (Hodge decomposition, and random clique complexes); and applications (arbitrage, and ranking of sports teams). Not all of these connections are explored in this paper, but many are. The underlying ideas are easy to explain, requiring only the four fundamental subspaces from elementary linear algebra. One of our aims is to explain these basic ideas and connections, to get researchers in many fields interested in this topic. Another aim is to use our numerical experiments for guidance on selecting methods and exposing the need for further development.

Connected through hyperedge HE-1 (induced by source paper "ranking a set of objects a graph based least square approach"):
  with members:
    - "graph resistance and learning from pairwise comparisons"
    - "maximum selection and ranking under noisy comparisons"
    - "active ranking from pairwise comparisons and when parametric assumptions don t help"
    - "top k ranking from pairwise comparisons when spectral ranking is optimal"
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "estimation from pairwise comparisons sharp minimax bounds with topology dependence"

Connected through hyperedge HE-2 (induced by source paper "hodge decomposition and the shapley value of a cooperative game"):
  with members:
    - "flows and decompositions of games harmonic and potential games"

Connected through hyperedge HE-3 (induced by source paper "rank aggregation for course sequence discovery"):
  with members:
    - "mapping the curricular structure and contents of network science courses"
    - "personalized course sequence recommendations"
    - "sync rank robust ranking constrained ranking and rank aggregation via eigenvector and semidefinite programming synchronization"
    - "methods for ordinal peer grading"
    - "pagerank beyond the web"
    - "rank centrality ranking from pair wise comparisons"

Connected through hyperedge HE-4 (induced by source paper "randomized kaczmarz for rank aggregation from pairwise comparisons"):
  with members:
    - "collaboratively learning preferences from ordinal data"
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "estimation from pairwise comparisons sharp minimax bounds with topology dependence"
    - "rank centrality ranking from pair wise comparisons"
    - "active ranking using pairwise comparisons"
    - "sorting from noisy information"

Connected through hyperedge HE-5 (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"
    - "rank aggregation via nuclear norm minimization"
    - "an inverse power method for nonlinear eigenproblems with applications in 1 spectral clustering and sparse pca"

Connected through hyperedge HE-6 (induced by source paper "sync rank robust ranking constrained ranking and rank aggregation via eigenvector and semidefinite programming synchronization"):
  with members:
    - "approximating the little grothendieck problem over the orthogonal and unitary groups"
    - "eigenvector synchronization graph rigidity and the molecule problem"
    - "active ranking using pairwise comparisons"
    - "the labor economics of paid crowdsourcing"
    - "noisy sorting without resampling"

Connected through hyperedge HE-7 (induced by source paper "fast adaptive algorithm for robust evaluation of quality of experience"):
  with members:
    - "robust binary fused compressive sensing using adaptive outlier pursuit"
    - "flows and decompositions of games harmonic and potential games"
    - "active ranking using pairwise comparisons"
    - "an active learning algorithm for ranking from pairwise preferences with an almost optimal query complexity"
    - "how i won the chess ratings elo vs the rest of the world competition"

Connected through hyperedge HE-8 (induced by source paper "pydec software and algorithms for discretization of exterior calculus"):
  with members:
    - "technical note delaunay hodge star"
    - "the numpy array a structure for efficient numerical computation"
    - "dolfin automated finite element computing"
    - "cohomologous harmonic cochains"
    - "the least spanning area of a knot and the optimal bounding chain problem"

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)