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: "nif a framework for quantifying neural information flow in deep networks"
  Abstract: In this paper, we present a new approach to interpreting deep learning models. More precisely, by coupling mutual information with network science, we explore how information flows through feed forward networks. We show that efficiently approximating mutual information via the dual representation of Kullback-Leibler divergence allows us to create an information measure that quantifies how much information flows between any two neurons of a deep learning model. To that end, we propose NIF, Neural Information Flow, a new metric for codifying information flow which exposes the internals of a deep learning model while providing feature attributions.

Connected through hyperedge HE-1 (induced by source paper "neural networks are surprisingly modular"):
  with members:
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "linear mode connectivity and the lottery ticket hypothesis"
    - "what s hidden in a randomly weighted neural network"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "learning both weights and connections for efficient neural networks"
    - "adam a method for stochastic optimization"
    - "a tutorial on spectral clustering"

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)