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: "scale invariant unconstrained online learning"
  Abstract: We consider a variant of online convex optimization in which both the instances (input vectors) and the comparator (weight vector) are unconstrained. We exploit a natural scale invariance symmetry in our unconstrained setting: the predictions of the optimal comparator are invariant under any linear transformation of the instances. Our goal is to design online algorithms which also enjoy this property, i.e. are scale-invariant. We start with the case of coordinate-wise invariance, in which the individual coordinates (features) can be arbitrarily rescaled. We give an algorithm, which achieves essentially optimal regret bound in this setup, expressed by means of a coordinate-wise scale-invariant norm of the comparator. We then study general invariance with respect to arbitrary linear transformations. We first give a negative result, showing that no algorithm can achieve a meaningful bound in terms of scale-invariant norm of the comparator in the worst case. Next, we compliment this result with a positive one, providing an algorithm which "almost" achieves the desired bound, incurring only a logarithmic overhead in terms of the norm of the instances.

Connected through hyperedge HE-1 (induced by source paper "adaptive online learning with varying norms"):
  with members:
    - "extreme tensoring for low memory preconditioning"
    - "adaptive scale invariant online algorithms for learning linear models"
    - "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"
    - "online learning without prior information"

Connected through hyperedge HE-2 (induced by source paper "lipschitz and comparator norm adaptivity in online learning"):
  with members:
    - "adaptive scale invariant online algorithms for learning linear models"
    - "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"
    - "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 "parameter free online convex optimization with sub exponential noise"):
  with members:
    - "black box reductions for parameter free online learning in banach spaces"
    - "online learning sufficient statistics and the burkholder method"
    - "training deep networks without learning rates through coin betting"
    - "online learning without prior information"
    - "coin betting and parameter free online learning"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"
    - "adaptive online learning"
    - "second order quantile methods for experts and combinatorial games"

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)