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: "trainability and data dependent initialization of over parameterized relu neural networks"
  Abstract: A neural network is said to be over-specified if its representational power is more than needed, and is said to be over-parameterized if the number of parameters is larger than the number of training data. In both cases, the number of neurons is larger than what it is necessary. In many applications, over-specified or over-parameterized neural networks are successfully employed and shown to be trained effectively. In this paper, we study the trainability of ReLU networks, a necessary condition for the successful training. We show that over-parameterization is both a necessary and a sufficient condition for minimizing the training loss. Specifically, we study the probability distribution of the number of active neurons at the initialization. We say a network is trainable if the number of active neurons is sufficiently large for a learning task. With this notion, we derive an upper bound of the probability of the successful training. Furthermore, we propose a data-dependent initialization method in the over-parameterized setting. Numerical examples are provided to demonstrate the effectiveness of the method and our theoretical findings.

Connected through hyperedge HE-1 (induced by source paper "nearly minimal over parametrization of shallow neural networks"):
  with members:
    - "quadratic suffices for over parametrization via matrix chernoff bound"
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "an improved analysis of training over parameterized deep neural networks"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "on learning over parameterized neural networks a functional approximation prospective"
    - "global convergence of adaptive gradient methods for an over parameterized neural network"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "on the power and limitations of random features for understanding neural networks"

Connected through hyperedge HE-2 (induced by source paper "generating accurate pseudo labels via hermite polynomials for ssl confidently"):
  with members:
    - "on the expressive power of deep polynomial neural networks"
    - "label propagation for deep semi supervised learning"
    - "joint optimization framework for learning with noisy labels"
    - "activation functions comparison of trends in practice and research for deep learning"
    - "saas speed as a supervisor for semi supervised learning"
    - "why relu networks yield high confidence predictions far away from the training data and how to mitigate the problem"
    - "learning one hidden layer neural networks with landscape design"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"

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)