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: "adaptive scale invariant online algorithms for learning linear models"
  Abstract: We consider online learning with linear models, where the algorithm predicts on sequentially revealed instances (feature vectors), and is compared against the best linear function (comparator) in hindsight. Popular algorithms in this framework, such as Online Gradient Descent (OGD), have parameters (learning rates), which ideally should be tuned based on the scales of the features and the optimal comparator, but these quantities only become available at the end of the learning process. In this paper, we resolve the tuning problem by proposing online algorithms making predictions which are invariant under arbitrary rescaling of the features. The algorithms have no parameters to tune, do not require any prior knowledge on the scale of the instances or the comparator, and achieve regret bounds matching (up to a logarithmic factor) that of OGD with optimally tuned separate learning rates per dimension, while retaining comparable runtime performance.

Connected through hyperedge HE-1 (induced by source paper "adaptive online learning with varying norms"):
  with members:
    - "extreme tensoring for low memory preconditioning"
    - "parameter free online convex optimization with sub exponential noise"
    - "matrix free preconditioning in online learning"
    - "shampoo preconditioned stochastic tensor optimization"
    - "black box reductions for parameter free online learning in banach spaces"
    - "online learning sufficient statistics and the burkholder method"
    - "scale invariant unconstrained online learning"
    - "online learning without prior information"

Connected through hyperedge HE-2 (induced by source paper "lipschitz and comparator norm adaptivity in online learning"):
  with members:
    - "introduction to online convex optimization"
    - "lipschitz adaptivity with multiple learning rates in online learning"
    - "black box reductions for parameter free online learning in banach spaces"
    - "online learning sufficient statistics and the burkholder method"
    - "scale invariant unconstrained online learning"
    - "zigzag a new approach to adaptive online learning"
    - "online learning without prior information"
    - "metagrad multiple learning rates in online learning"

Connected through hyperedge HE-3 (induced by source paper "online learning with imperfect hints"):
  with members:
    - "near optimal bounds for online caching with machine learned advice"
    - "parameter free online convex optimization with sub exponential noise"
    - "matrix free preconditioning in online learning"
    - "semi online bipartite matching"
    - "black box reductions for parameter free online learning in banach spaces"
    - "competitive caching with machine learned advice"
    - "online learning sufficient statistics and the burkholder method"
    - "coin betting and parameter free online learning"

Connected through hyperedge HE-4 (induced by source paper "matrix free preconditioning in online learning"):
  with members:
    - "shampoo preconditioned stochastic tensor optimization"
    - "black box reductions for parameter free online learning in banach spaces"
    - "the case for full matrix adaptive regularization"
    - "attention is all you need"
    - "training deep networks without learning rates through coin betting"
    - "online learning without prior information"
    - "efficient second order online learning by sketching"
    - "coin betting and parameter free online learning"

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)