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: "stuck in a what adventures in weight space"
  Abstract: Deep learning researchers commonly suggest that converged models are stuck in local minima. More recently, some researchers observed that under reasonable assumptions, the vast majority of critical points are saddle points, not true minima. Both descriptions suggest that weights converge around a point in weight space, be it a local optima or merely a critical point. However, it’s possible that neither interpretation is accurate. As neural networks are typically over-complete, it’s easy to show the existence of vast continuous regions through weight space with equal loss. In this paper, we build on recent work empirically characterizing the error surfaces of neural networks. We analyze training paths through weight space, presenting evidence that apparent convergence of loss does not correspond to weights arriving at critical points, but instead to large movements through flat regions of weight space. While it’s trivial to show that neural network error surfaces are globally non-convex, we show that error surfaces are also locally nonconvex, even after breaking symmetry with a random initialization and also after partial training.

Connected through hyperedge HE-1 (induced by source paper "classifying the classifier dissecting the weight space of neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "topology of learning in artificial neural networks"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "taskonomy disentangling task transfer learning"
    - "darts differentiable architecture search"
    - "do convolutional neural networks learn class hierarchy"
    - "network dissection quantifying interpretability of deep visual representations"
    - "interpreting deep visual representations via network dissection"

Connected through hyperedge HE-2 (induced by source paper "luck matters understanding training dynamics of deep relu networks"):
  with members:
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "a jamming transition from under to over parametrization affects generalization in deep learning"
    - "measuring the intrinsic dimension of objective landscapes"
    - "deep learning is robust to massive label noise"
    - "theoretical analysis of auto rate tuning by batch normalization"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "a theoretical framework for deep and locally connected relu network"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "a jamming transition from under to over parametrization affects generalization in deep learning"):
  with members:
    - "two models of double descent for weak features"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "the loss landscape of overparameterized neural networks"
    - "neural tangent kernel convergence and generalization in neural networks"
    - "geometry of optimization and implicit regularization in deep learning"
    - "empirical analysis of the hessian of over parametrized neural networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-4 (induced by source paper "visualizing the loss landscape of neural nets"):
  with members:
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "theoretical insights into the optimization landscape of over parameterized shallow neural networks"
    - "the shattered gradients problem if resnets are the answer then what is the question"
    - "depth creates no bad local minima"
    - "the loss surface of deep and wide neural networks"
    - "global optimality conditions for deep neural networks"
    - "an analytical formula of population gradient for two layered relu network and its applications in convergence and critical point analysis"
    - "accurate large minibatch sgd training imagenet in 1 hour"

Connected through hyperedge HE-5 (induced by source paper "distributed bayesian learning with stochastic natural gradient expectation propagation and the posterior server"):
  with members:
    - "overcoming catastrophic forgetting in neural networks"
    - "adam a method for stochastic optimization"
    - "neural variational inference and learning in belief networks"
    - "identifying and attacking the saddle point problem in high dimensional non convex optimization"
    - "austerity in mcmc land cutting the metropolis hastings budget"
    - "expectation propogation for approximate inference in dynamic bayesian networks"

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)