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: "a new perspective on machine learning how to do perfect supervised learning"
  Abstract: In this work, we introduce the concept of bandlimiting into the theory of machine learning because all physical processes are bandlimited by nature, including real-world machine learning tasks. After the bandlimiting constraint is taken into account, our theoretical analysis has shown that all practical machine learning tasks are asymptotically solvable in a perfect sense. Furthermore, the key towards this solvability almost solely relies on two factors: i) a sufficiently large amount of training samples beyond a threshold determined by a difficulty measurement of the underlying task; ii) a sufficiently complex and bandlimited model. Moreover, for some special cases, we have derived new error bounds for perfect learning, which can quantify the difficulty of learning. These generalization bounds are not only asymptotically convergent but also irrelevant to model complexity. Our new results on generalization have provided a new perspective to explain the recent successes of large-scale supervised learning using complex models like neural networks.

Connected through hyperedge HE-1 (induced by source paper "frequency based search control in dyna"):
  with members:
    - "hill climbing on value estimates for search control in dyna"
    - "efficient model based deep reinforcement learning with variational state tabulation"
    - "organizing experience a deeper look at replay mechanisms for sample based planning in continuous state domains"
    - "recurrent world models facilitate policy evolution"
    - "value prediction network"
    - "the predictron end to end learning and planning"
    - "continuous deep q learning with model based acceleration"
    - "self correcting models for model based reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "bandlimiting neural networks against adversarial attacks"):
  with members:
    - "on evaluating adversarial robustness"
    - "on the robustness of the cvpr 2018 white box adversarial example defenses"
    - "genattack practical black box attacks with gradient free optimization"
    - "deflecting adversarial attacks with pixel deflection"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "defense against adversarial attacks using high level representation guided denoiser"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"

Connected through hyperedge HE-3 (induced by source paper "why learning of large scale neural networks behaves like convex optimization"):
  with members:
    - "gradient descent finds global minima of deep neural networks"
    - "stochastic gradient descent optimizes over parameterized deep relu networks"
    - "a convergence theory for deep learning via over parameterization"
    - "gradient descent provably optimizes over parameterized neural networks"
    - "the loss surface of deep and wide neural networks"
    - "spurious local minima are common in two layer relu neural networks"
    - "global optimality in tensor factorization deep learning and beyond"
    - "qualitatively characterizing neural network optimization problems"

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)