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: "large scale convex minimization with a low rank constraint"
  Abstract: We address the problem of minimizing a convex function over the space of large matrices with low rank. While this optimization problem is hard in general, we propose an efficient greedy algorithm and derive its formal approximation guarantees. Each iteration of the algorithm involves (approximately) finding the left and right singular vectors corresponding to the largest singular value of a certain matrix, which can be calculated in linear time. This leads to an algorithm which can scale to large matrices arising in several applications such as matrix completion for collaborative filtering and robust low rank matrix approximation.

Connected through hyperedge HE-1 (induced by source paper "introduction to online convex optimization"):
  with members:
    - "online linear optimization via smoothing"
    - "relative loss bounds for on line density estimation with the exponential family of distributions"
    - "block coordinate frank wolfe optimization for structural svms"
    - "online bandit learning against an adaptive adversary from regret to policy regret"
    - "projection free online learning"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes"
    - "making gradient descent optimal for strongly convex stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "improved regret bounds for projection free bandit convex optimization"):
  with members:
    - "introduction to online convex optimization"
    - "linear convergence of a frank wolfe type algorithm over trace norm balls"
    - "kernel based methods for bandit convex optimization"
    - "an optimal algorithm for bandit convex optimization"
    - "bandit convex optimization sqrt t regret in one dimension"
    - "a hybrid algorithm for convex semidefinite optimization"
    - "block coordinate frank wolfe optimization for structural svms"
    - "projection free online learning"

Connected through hyperedge HE-3 (induced by source paper "hashing with binary matrix pursuit"):
  with members:
    - "hashing with mutual information"
    - "mihash online hashing with mutual information"
    - "hashnet deep learning to hash by continuation"
    - "large scale binary quadratic optimization using semidefinite relaxation and applications"
    - "fast training of triplet based deep binary embedding networks"
    - "learning of generalized low rank models a greedy approach"
    - "deep supervised hashing with triplet labels"
    - "binary hashing with semidefinite relaxation and augmented lagrangian"

Connected through hyperedge HE-4 (induced by source paper "revisiting projection free optimization for strongly convex constraint sets"):
  with members:
    - "introduction to online convex optimization"
    - "following the leader and fast rates in linear prediction curved constraint sets and other regularities"
    - "minding the gaps for block frank wolfe optimization of structured svms"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "robust optimization for deep regression"
    - "on pairwise costs for network flow multi object tracking"
    - "projection free online learning"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-5 (induced by source paper "low rank methods in event detection"):
  with members:
    - "the numpy array a structure for efficient numerical computation"
    - "matrix completion from a few entries"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-6 (induced by source paper "failures of gradient based deep learning"):
  with members:
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "distribution specific hardness of learning neural networks"
    - "on the sample complexity of end to end training vs semantic abstraction training"
    - "wavenet a generative model for raw audio"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "trust region policy optimization"

Connected through hyperedge HE-7 (induced by source paper "differentially private matrix completion revisited"):
  with members:
    - "fast matrix completion without the condition number"
    - "large scale multi label learning with missing labels"
    - "calibrating data to sensitivity in private data analysis"
    - "mechanism design in large games incentives and privacy"
    - "beating randomized response on incoherent matrices"
    - "beyond worst case analysis in private singular vector computation"
    - "guaranteed rank minimization via singular value projection"
    - "matrix completion from a few entries"

Connected through hyperedge HE-8 (induced by source paper "linear convergence of a frank wolfe type algorithm over trace norm balls"):
  with members:
    - "lazysvd even faster svd decomposition yet without agonizing pain"
    - "randomized block krylov methods for stronger and faster approximate singular value decomposition"
    - "a linearly convergent conditional gradient algorithm with applications to online and stochastic optimization"
    - "exact matrix completion via convex optimization"

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)