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: "on the complexity of nonnegative matrix factorization"
  Abstract: Nonnegative matrix factorization (NMF) has become a prominent technique for the analysis of image databases, text databases and other information retrieval and clustering applications. In this report, we define an exact version of NMF. Then we establish several results about exact NMF: (1) that it is equivalent to a problem in polyhedral combinatorics; (2) that it is NP-hard; and (3) that a polynomial-time local search heuristic exists.

Connected through hyperedge HE-1 (induced by source paper "convergence to second order stationarity for non negative matrix factorization provably and concurrently"):
  with members:
    - "how to escape saddle points efficiently"
    - "matrix completion has no spurious local minimum"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "descent methods for nonnegative matrix factorization"
    - "computing a nonnegative matrix factorization provably"

Connected through hyperedge HE-2 (induced by source paper "capuchin causal database repair for algorithmic fairness"):
  with members:
    - "causal reasoning for algorithmic fairness"
    - "fairness and accountability design needs for algorithmic support in high stakes public sector decision making"
    - "bias in olap queries detection explanation and removal"
    - "fairness testing testing software for discrimination"
    - "computing optimal repairs for functional dependencies"
    - "learning non discriminatory predictors"
    - "algorithmic decision making and the cost of fairness"
    - "equality of opportunity in supervised learning"

Connected through hyperedge HE-3 (induced by source paper "compressed factorization fast and accurate low rank factorization of compressively sensed data"):
  with members:
    - "a practical randomized cp tensor decomposition"
    - "orthogonalized als a theoretically principled tensor decomposition algorithm for practical use"
    - "compressed nonnegative matrix factorization is fast and accurate"
    - "stable robust and super fast reconstruction of tensors using multi way projections"
    - "guaranteed non orthogonal tensor decomposition via alternating rank 1 updates"
    - "low rank approximation and regression in input sparsity time"
    - "learning topic models going beyond svd"
    - "exact recovery of sparsely used dictionaries"

Connected through hyperedge HE-4 (induced by source paper "learning distributions generated by one layer relu networks"):
  with members:
    - "learning one convolutional layer with overlapping patches"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "learning two layer neural networks with symmetric inputs"
    - "learning relus via gradient descent"
    - "recovery guarantees for one hidden layer neural networks"
    - "convergence analysis of two layer neural networks with relu activation"
    - "pixel recurrent neural networks"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "pd ml lite private distributed machine learning from lighweight cryptography"):
  with members:
    - "a flexible privacy preserving framework for singular value decomposition under internet of things environment"
    - "efficient hyperparameter optimization and infinitely many armed bandits"
    - "typicality based stability and privacy"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "fast differentially private matrix factorization"
    - "a new approach to practical active secure two party computation"
    - "beyond worst case analysis in private singular vector computation"
    - "distributed learning communication complexity and privacy"

Connected through hyperedge HE-6 (induced by source paper "generalized separable nonnegative matrix factorization"):
  with members:
    - "on identifiability of nonnegative matrix factorization"
    - "hierarchical clustering of hyperspectral images using rank two nonnegative matrix factorization"
    - "learning the information divergence"
    - "a practical algorithm for topic modeling with provable guarantees"
    - "learning topic models going beyond svd"
    - "computing a nonnegative matrix factorization provably"
    - "non negative matrix factorization with sparseness constraints"

Connected through hyperedge HE-7 (induced by source paper "online convex matrix factorization with representative regions"):
  with members:
    - "a ranking model motivated by nonnegative matrix factorization with applications to tennis tournaments"
    - "symmetry saddle points and global geometry of nonconvex matrix factorization"
    - "sparse approximation via generating point sets"
    - "simultaneous codeword optimization simco for dictionary update and learning"
    - "convex sparse matrix factorizations"

Connected through hyperedge HE-8 (induced by source paper "a quantum inspired classical algorithm for separable non negative matrix factorization"):
  with members:
    - "a quantum inspired classical algorithm for recommendation systems"
    - "quantum inspired low rank stochastic regression with logarithmic dependence on the dimension"
    - "quantum inspired sublinear classical algorithms for solving low rank linear systems"
    - "learning topic models going beyond svd"
    - "computing a nonnegative matrix factorization provably"

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)