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: "geometrization of deep networks for the interpretability of deep learning systems"
  Abstract: How to understand deep learning systems remains an open problem. In this paper we propose that the answer may lie in the geometrization of deep networks. Geometrization is a bridge to connect physics, geometry, deep network and quantum computation and this may result in a new scheme to reveal the rule of the physical world. By comparing the geometry of image matching and deep networks, we show that geometrization of deep networks can be used to understand existing deep learning systems and it may also help to solve the interpretability problem of deep learning systems.

Connected through hyperedge HE-1 (induced by source paper "deep network as memory space complexity generalization disentangled representation and interpretability"):
  with members:
    - "dataset distillation"
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "weight agnostic neural networks"
    - "understanding over parameterized deep networks by geometrization"
    - "gauge theory and twins paradox of disentangled representations"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "gan dissection visualizing and understanding generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "understanding over parameterized deep networks by geometrization"):
  with members:
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "the loss landscape of overparameterized neural networks"
    - "a convergence theory for deep learning via over parameterization"
    - "gradient descent provably optimizes over parameterized neural networks"
    - "how deep learning works the geometry of deep learning"
    - "fisher rao metric geometry and complexity of neural networks"
    - "towards understanding generalization of deep learning perspective of loss landscapes"

Connected through hyperedge HE-3 (induced by source paper "gauge theory and twins paradox of disentangled representations"):
  with members:
    - "understanding over parameterized deep networks by geometrization"
    - "towards a definition of disentangled representations"
    - "how deep learning works the geometry of deep 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)