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: "provable limitations of deep learning"
  Abstract: As the success of deep learning reaches more grounds, one would like to also envision the potential limits of deep learning. This paper gives a first set of results proving that certain deep learning algorithms fail at learning certain efficiently learnable functions. The results put forward a notion of cross-predictability that characterizes when such failures take place. Parity functions provide an extreme example with a cross-predictability that decays exponentially, while a mere super-polynomial decay of the cross-predictability is shown to be sufficient to obtain failures. Examples in community detection and arithmetic learning are also discussed. #R##N#Recall that it is known that the class of neural networks (NNs) with polynomial network size can express any function that can be implemented in polynomial time, and that their sample complexity scales polynomially with the network size. The challenge is with the optimization error (the ERM is NP-hard), and the success behind deep learning is to train deep NNs with descent algorithms. The failures shown in this paper apply to training poly-size NNs on function distributions of low cross-predictability with a descent algorithm that is either run with limited memory per sample or that is initialized and run with enough randomness. We further claim that such types of constraints are necessary to obtain failures, in that exact SGD with careful non-random initialization can be shown to learn parities. The cross-predictability in our results plays a similar role the statistical dimension in statistical query (SQ) algorithms, with distinctions explained in the paper. The proof techniques are based on exhibiting algorithmic constraints that imply a statistical indistinguishability between the algorithm's output on the test model v.s.\ a null model, using information measures to bound the total variation distance.

Connected through hyperedge HE-1 (induced by source paper "poly time universality and limitations of deep learning"):
  with members:
    - "gradient descent for one hidden layer neural networks polynomial convergence and sq lower bounds"
    - "on the complexity of learning neural networks"
    - "extractor based time space lower bounds for learning"
    - "time space tradeoffs for learning from small test spaces learning low degree polynomial functions"
    - "understanding deep learning requires rethinking generalization"
    - "distribution specific hardness of learning neural networks"
    - "statistical query algorithms for mean vector estimation and stochastic convex optimization"
    - "train faster generalize better stability of stochastic gradient descent"

Connected through hyperedge HE-2 (induced by source paper "learning boolean circuits with neural networks"):
  with members:
    - "decoupled greedy learning of cnns"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "id3 learns juntas for smoothed product distributions"
    - "on the learnability of deep random networks"
    - "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"
    - "a comparative analysis of the optimization and generalization property of two layer neural network and random feature models under gradient descent dynamics"
    - "what can resnet learn efficiently going beyond kernels"

Connected through hyperedge HE-3 (induced by source paper "ten ai stepping stones for cybersecurity"):
  with members:
    - "machine learning ddos detection for consumer internet of things devices"
    - "adversarial deep learning for robust detection of binary encoded malware"
    - "cache telepathy leveraging shared resource attacks to learn dnn architectures"
    - "adversarial malware binaries evading deep learning for malware detection in executables"
    - "a first look at deep learning apps on smartphones"
    - "prada protecting against dnn model stealing attacks"
    - "logan membership inference attacks against generative models"
    - "malicious url detection using machine learning a survey"

Connected through hyperedge HE-4 (induced by source paper "depth separations in neural networks what is actually being separated"):
  with members:
    - "on the complexity of learning neural networks"
    - "failures of gradient based deep learning"
    - "depth separation for neural networks"
    - "error bounds for approximations with deep relu networks"
    - "distribution specific hardness of learning neural networks"
    - "benefits of depth in neural networks"
    - "why deep neural networks for function approximation"
    - "depth width tradeoffs in approximating natural functions with neural networks"

Connected through hyperedge HE-5 (induced by source paper "fast uap algorithm for speeding up universal adversarial perturbation generation with orientation of perturbation vectors"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "end to end learning for self driving cars"
    - "delving into transferable adversarial examples and black box attacks"
    - "towards evaluating the robustness of neural networks"
    - "adversarial machine learning at scale"
    - "deep neural network based malware detection using two dimensional binary program features"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "efficient deep learning of gmms"):
  with members:
    - "the power of deeper networks for expressing natural functions"
    - "the power of depth for feedforward neural networks"
    - "representation benefits of deep feedforward networks"
    - "on the expressive power of deep learning a tensor analysis"
    - "on the expressive efficiency of sum product networks"

Connected through hyperedge HE-7 (induced by source paper "quantifying point prediction uncertainty in neural networks via residual estimation with an i o kernel"):
  with members:
    - "conditional neural processes"
    - "deep kernel learning"
    - "gaussian processes for big data"

Connected through hyperedge HE-8 (induced by source paper "shield fast practical defense and vaccination for deep learning using jpeg compression"):
  with members:
    - "dense associative memory is robust to adversarial inputs"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial attacks on neural network policies"
    - "robust physical world attacks on deep learning models"
    - "tactics of adversarial attack on deep reinforcement learning agents"
    - "enhancing robustness of machine learning systems via data transformations"
    - "keeping the bad guys out protecting and vaccinating deep learning with jpeg compression"

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)