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: "complex valued autoencoders"
  Abstract: Autoencoders are unsupervised machine learning circuits, with typically one hidden layer, whose learning goal is to minimize an average distortion measure between inputs and outputs. Linear autoencoders correspond to the special case where only linear transformations between visible and hidden variables are used. While linear autoencoders can be defined over any field, only real-valued linear autoencoders have been studied so far. Here we study complex-valued linear autoencoders where the components of the training vectors and adjustable matrices are defined over the complex field with the L"2 norm. We provide simpler and more general proofs that unify the real-valued and complex-valued cases, showing that in both cases the landscape of the error function is invariant under certain groups of transformations. The landscape has no local minima, a family of global minima associated with Principal Component Analysis, and many families of saddle points associated with orthogonal projections onto sub-space spanned by sub-optimal subsets of eigenvectors of the covariance matrix. The theory yields several iterative, convergent, learning algorithms, a clear understanding of the generalization properties of the trained autoencoders, and can equally be applied to the hetero-associative case when external targets are provided. Partial results on deep architecture as well as the differential geometry of autoencoders are also presented. The general framework described here is useful to classify autoencoders and identify general properties that ought to be investigated for each class, illuminating some of the connections between autoencoders, unsupervised learning, clustering, Hebbian learning, and information theory.

Connected through hyperedge HE-1 (induced by source paper "deep neural networks with multi branch architectures are less non convex"):
  with members:
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "on the power of over parametrization in neural networks with quadratic activation"
    - "learning one hidden layer neural networks with landscape design"
    - "matrix completion and related problems via strong duality"
    - "mathematics of deep learning"
    - "theoretical insights into the optimization landscape of over parameterized shallow neural networks"
    - "failures of gradient based deep learning"

Connected through hyperedge HE-2 (induced by source paper "analytic network learning"):
  with members:
    - "implicit bias of gradient descent on linear convolutional networks"
    - "learning from the kernel and the range space"
    - "exploring generalization in deep learning"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "implicit regularization in deep learning"
    - "a pac bayesian approach to spectrally normalized margin bounds for neural networks"
    - "sharp minima can generalize for deep nets"
    - "spectrally normalized margin bounds for neural networks"

Connected through hyperedge HE-3 (induced by source paper "the multilinear structure of relu networks"):
  with members:
    - "learning relus via gradient descent"
    - "deep linear neural networks with arbitrary loss all local minima are global"
    - "recovery guarantees for one hidden layer neural networks"
    - "the loss surface of deep and wide neural networks"
    - "an analytical formula of population gradient for two layered relu network and its applications in convergence and critical point analysis"
    - "sgd learns over parameterized networks that provably generalize on linearly separable data"
    - "when is a convolutional filter easy to learn"
    - "convergence analysis of two layer neural networks with relu activation"

Connected through hyperedge HE-4 (induced by source paper "deep linear neural networks with arbitrary loss all local minima are global"):
  with members:
    - "depth creates no bad local minima"
    - "identity matters in deep learning"
    - "exact solutions to the nonlinear dynamics of learning in deep linear neural networks"

Connected through hyperedge HE-5 (induced by source paper "deep online convex optimization with gated games"):
  with members:
    - "efficient second order online learning by sketching"
    - "toward deeper understanding of neural networks the power of initialization and a dual view on expressivity"
    - "benefits of depth in neural networks"
    - "ensemble robustness of deep learning algorithms"
    - "strongly typed recurrent neural networks"
    - "conditional computation in neural networks for faster models"
    - "the power of depth for feedforward neural networks"
    - "deep learning in neural networks"

Connected through hyperedge HE-6 (induced by source paper "learning in the machine random backpropagation and the deep learning channel"):
  with members:
    - "learning in the machine the symmetries of the deep learning channel"
    - "a theory of local learning the learning channel and the optimality of backpropagation"
    - "quantized neural networks training neural networks with low precision weights and activations"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "deep residual learning for image recognition"
    - "training very deep networks"
    - "how important is weight symmetry in backpropagation"
    - "going deeper with convolutions"

Connected through hyperedge HE-7 (induced by source paper "an empirical analysis of the optimization of deep network loss surfaces"):
  with members:
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"
    - "qualitatively characterizing neural network optimization problems"
    - "very deep convolutional networks for large scale image recognition"
    - "identifying and attacking the saddle point problem in high dimensional non convex optimization"
    - "imagenet large scale visual recognition challenge"
    - "exact solutions to the nonlinear dynamics of learning in deep linear neural networks"

Connected through hyperedge HE-8 (induced by source paper "piecewise convexity of artificial neural networks"):
  with members:
    - "escaping from saddle points online stochastic gradient for tensor decomposition"

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.NE(Neural and Evolutionary Computing)