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: "how deep learning works the geometry of deep learning"
  Abstract: Why and how that deep learning works well on different tasks remains a mystery from a theoretical perspective. In this paper we draw a geometric picture of the deep learning system by finding its analogies with two existing geometric structures, the geometry of quantum computations and the geometry of the diffeomorphic template matching. In this framework, we give the geometric structures of different deep learning systems including convolutional neural networks, residual networks, recursive neural networks, recurrent neural networks and the equilibrium prapagation framework. We can also analysis the relationship between the geometrical structures and their performance of different networks in an algorithmic level so that the geometric framework may guide the design of the structures and algorithms 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"
    - "geometrization of deep networks for the interpretability of deep learning systems"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "geometrization of deep networks for the interpretability of deep learning systems"):
  with members:
    - "adversarial noise layer regularize neural network by adding noise"
    - "relational inductive biases deep learning and graph networks"
    - "learning to reweight examples for robust deep learning"
    - "neural ordinary differential equations"
    - "fisher rao metric geometry and complexity of neural networks"
    - "equilibrium propagation bridging the gap between energy based models and backpropagation"
    - "the euler poincare theory of metamorphosis"

Connected through hyperedge HE-3 (induced by source paper "understanding over parameterized deep networks by geometrization"):
  with members:
    - "geometrization of deep networks for the interpretability of deep learning systems"
    - "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"
    - "fisher rao metric geometry and complexity of neural networks"
    - "towards understanding generalization of deep learning perspective of loss landscapes"

Connected through hyperedge HE-4 (induced by source paper "gauge theory and twins paradox of disentangled representations"):
  with members:
    - "geometrization of deep networks for the interpretability of deep learning systems"
    - "understanding over parameterized deep networks by geometrization"
    - "towards a definition of disentangled representations"

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)