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: "noise tolerant learning the parity problem and the statistical query model"
  Abstract: We describe a slightly sub-exponential time algorithm for learning parity functions in the presence of random classification noise. This results in a polynomial-time algorithm for the case of parity functions that depend on only the first O(log n log log n) bits of input. This is the first known instance of an efficient noise-tolerant algorithm for a concept class that is provably not learnable in the Statistical Query model of Kearns. Thus, we demonstrate that the set of problems learnable in the statistical query model is a strict subset of those problems learnable in the presence of noise in the PAC model. #R##N#In coding-theory terms, what we give is a poly(n)-time algorithm for decoding linear k by n codes in the presence of random noise for the case of k = c log n loglog n for some c > 0. (The case of k = O(log n) is trivial since one can just individually check each of the 2^k possible messages and choose the one that yields the closest codeword.) #R##N#A natural extension of the statistical query model is to allow queries about statistical properties that involve t-tuples of examples (as opposed to single examples). The second result of this paper is to show that any class of functions learnable (strongly or weakly) with t-wise queries for t = O(log n) is also weakly learnable with standard unary queries. Hence this natural extension to the statistical query model does not increase the set of weakly learnable functions.

Connected through hyperedge HE-1 (induced by source paper "combating noisy labels by agreement a joint training method with co regularization"):
  with members:
    - "partial label learning with self guided retraining"
    - "learning with inadequate and incorrect supervision"
    - "are anchor points really indispensable in label noise learning"
    - "nlnl negative learning for noisy labels"
    - "how does disagreement help generalization against label corruption"
    - "probabilistic end to end noise correction for learning with noisy labels"
    - "joint optimization framework for learning with noisy labels"
    - "learning to reweight examples for robust deep learning"

Connected through hyperedge HE-2 (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"
    - "provable limitations of deep learning"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "qactor on line active learning for noisy labeled stream data"):
  with members:
    - "dimensionality driven learning with noisy labels"
    - "online deep learning learning deep neural networks on the fly"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "deep bayesian active learning with image data"
    - "training deep neural networks on noisy labels with bootstrapping"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-4 (induced by source paper "noisy simon period finding"):
  with members:
    - "an improved bkw algorithm for lwe with applications to cryptography and lattices"

Connected through hyperedge HE-5 (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-6 (induced by source paper "id3 learns juntas for smoothed product distributions"):
  with members:
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "a comparative analysis of the optimization and generalization property of two layer neural network and random feature models under gradient descent dynamics"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "overparameterized nonlinear learning gradient descent takes the shortest path"
    - "beyond the low degree algorithm mixtures of subcubes and their applications"
    - "a convergence theory for deep learning via over parameterization"
    - "gradient descent provably optimizes over parameterized neural networks"

Connected through hyperedge HE-7 (induced by source paper "learning to collaborate in markov decision processes"):
  with members:
    - "modeling others using oneself in multi agent reinforcement learning"
    - "an overview of machine teaching"
    - "online reinforcement learning in stochastic games"
    - "game theoretic modeling of human adaptation in human robot collaboration"
    - "corralling a band of bandit algorithms"
    - "prediction with a short memory"
    - "fast convergence of regularized learning in games"
    - "online learning in markov decision processes with adversarially chosen transition probability distributions"

Connected through hyperedge HE-8 (induced by source paper "algebraic aspects of solving ring lwe including ring based improvements in the blum kalai wasserman algorithm"):
  with members:
    - "attacks on the search rlwe problem with small errors"
    - "an improved bkw algorithm for lwe with applications to cryptography and lattices"
    - "provably weak instances of ring lwe"

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)