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: "the optimal sample complexity of pac learning"
  Abstract: This work establishes a new upper bound on the number of samples sufficient for PAC learning in the realizable case. The bound matches known lower bounds up to numerical constant factors. This solves a long-standing open problem on the sample complexity of PAC learning. The technique and analysis build on a recent breakthrough by Hans Simon.

Connected through hyperedge HE-1 (induced by source paper "beyond dropout feature map distortion to regularize deep neural networks"):
  with members:
    - "language models with transformers"
    - "dropblock a regularization method for convolutional networks"
    - "autoaugment learning augmentation policies from data"
    - "improved regularization of convolutional neural networks with cutout"
    - "identity mappings in deep residual networks"
    - "convolutional two stream network fusion for video action recognition"
    - "fractalnet ultra deep neural networks without residuals"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-2 (induced by source paper "shapley homology topological analysis of sample influence for neural networks"):
  with members:
    - "dataset distillation"
    - "adversarial attack on graph structured data"
    - "verification of recurrent neural networks through rule extraction"
    - "representer point selection for explaining deep neural networks"
    - "learning to reweight examples for robust deep learning"
    - "data poisoning attacks against online learning"
    - "an empirical evaluation of rule extraction from recurrent neural networks"
    - "approximating the spectrum of a graph"

Connected through hyperedge HE-3 (induced by source paper "analysis on the nonlinear dynamics of deep neural networks topological entropy and chaos"):
  with members:
    - "sharp minima can generalize for deep nets"
    - "understanding deep learning requires rethinking generalization"
    - "why deep neural networks for function approximation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "tight bounds for collaborative pac learning via multiplicative weights"):
  with members:
    - "improved algorithms for collaborative pac learning"
    - "distributed partial clustering"
    - "improved distributed principal component analysis"
    - "distributed learning communication complexity and privacy"

Connected through hyperedge HE-5 (induced by source paper "effective parallelisation for machine learning"):
  with members:
    - "distributed learning with regularized least squares"
    - "openml networked science in machine learning"
    - "communication efficient distributed optimization using an approximate newton type method"
    - "optimal distributed online prediction using mini batches"

Connected through hyperedge HE-6 (induced by source paper "a new lower bound for agnostic learning with sample compression schemes"):
  with members:
    - "sample compression schemes for vc classes"

Connected through hyperedge HE-7 (induced by source paper "active classification with comparison queries"):
  with members:
    - "interactive bayesian hierarchical clustering"
    - "dimension coupling optimal active learning of halfspaces via query synthesis"
    - "clustering with same cluster queries"
    - "beyond disagreement based agnostic active learning"
    - "a compression technique for analyzing disagreement based active learning"
    - "minimax analysis of active learning"
    - "learning using local membership queries"
    - "efficient active learning of halfspaces an aggressive approach"

Connected through hyperedge HE-8 (induced by source paper "combating deep reinforcement learning s sisyphean curse with intrinsic fear"):
  with members:
    - "efficient exploration through bayesian deep q networks"
    - "safe multi agent reinforcement learning for autonomous driving"
    - "efficient exploration for dialog policy learning with deep bbq networks replay buffer spiking"
    - "bbq networks efficient exploration in deep reinforcement learning for task oriented dialogue systems"
    - "policy networks with two stage training for dialogue systems"
    - "unifying count based exploration and intrinsic motivation"
    - "end to end training of deep visuomotor policies"
    - "adam a method for stochastic optimization"

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)