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: "generalized inner loop meta learning"
  Abstract: Many (but not all) approaches self-qualifying as "meta-learning" in deep learning and reinforcement learning fit a common pattern of approximating the solution to a nested optimization problem. In this paper, we give a formalization of this shared pattern, which we call GIMLI, prove its general requirements, and derive a general-purpose algorithm for implementing similar approaches. Based on this analysis and algorithm, we describe a library of our design, higher, which we share with the community to assist and enable future research into these kinds of meta-learning approaches. We end the paper by showcasing the practical applications of this framework and library through illustrative experiments and ablation studies which they facilitate.

Connected through hyperedge HE-1 (induced by source paper "learning state dependent losses for inverse dynamics learning"):
  with members:
    - "encoding physical constraints in differentiable newton euler algorithm"
    - "meta learning via learned loss"
    - "pytorch an imperative style high performance deep learning library"
    - "evolved policy gradients"
    - "deep online learning via meta learning continual adaptation for model based rl"
    - "online learning of a memory for learning rates"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "learning to learn by gradient descent by gradient descent"

Connected through hyperedge HE-2 (induced by source paper "optimizing millions of hyperparameters by implicit differentiation"):
  with members:
    - "dataset distillation"
    - "on solving minimax optimization locally a follow the ridge approach"
    - "meta learning with implicit gradients"
    - "deep equilibrium models"
    - "autoassist a framework to accelerate training of deep neural networks"
    - "self tuning networks bilevel optimization of hyperparameters using structured best response functions"
    - "adversarial learning of general transformations for data augmentation"
    - "on first order meta learning algorithms"

Connected through hyperedge HE-3 (induced by source paper "gradslam dense slam meets automatic differentiation"):
  with members:
    - "end to end lane detection through differentiable least squares fitting"
    - "scenecode monocular dense semantic reconstruction using learned encoded scene representations"
    - "kornia an open source differentiable computer vision library for pytorch"
    - "cubeslam monocular 3 d object slam"
    - "deeptam deep tracking and mapping"
    - "regnet learning the optimization of direct image to image pose registration"
    - "constructing category specific models for monocular object slam"
    - "codeslam learning a compact optimisable representation for dense visual slam"

Connected through hyperedge HE-4 (induced by source paper "meta learning via learned loss"):
  with members:
    - "guided meta policy search"
    - "reward shaping via meta learning"
    - "deep lagrangian networks using physics as model prior for deep learning"
    - "meta gradient reinforcement learning"
    - "unsupervised learning via meta learning"
    - "meta reinforcement learning of structured exploration strategies"
    - "evolved policy gradients"
    - "one shot imitation from observing humans via domain adaptive meta 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)