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: "drmad distilling reverse mode automatic differentiation for optimizing hyperparameters of deep neural networks"
  Abstract: The performance of deep neural networks is well-known to be sensitive to the setting of their hyperparameters. Recent advances in reverse-mode automatic differentiation allow for optimizing hyperparameters with gradients. The standard way of computing these gradients involves a forward and backward pass of computations. However, the backward pass usually needs to consume unaffordable memory to store all the intermediate variables to exactly reverse the forward training procedure. In this work we propose a simple but effective method, DrMAD, to distill the knowledge of the forward pass into a shortcut path, through which we approximately reverse the training trajectory. Experiments on several image benchmark datasets show that DrMAD is at least 45 times faster and consumes 100 times less memory compared to state-of-the-art methods for optimizing hyperparameters with minimal compromise to its effectiveness. To the best of our knowledge, DrMAD is the first research attempt to make it practical to automatically tune thousands of hyperparameters of deep neural networks. The code can be downloaded from this https URL

Connected through hyperedge HE-1 (induced by source paper "optimizing millions of hyperparameters by implicit differentiation"):
  with members:
    - "dataset distillation"
    - "on solving minimax optimization locally a follow the ridge approach"
    - "meta learning with implicit gradients"
    - "deep equilibrium models"
    - "autoassist a framework to accelerate training of deep neural networks"
    - "generalized inner loop meta learning"
    - "self tuning networks bilevel optimization of hyperparameters using structured best response functions"
    - "adversarial learning of general transformations for data augmentation"

Connected through hyperedge HE-2 (induced by source paper "reducing the search space for hyperparameter optimization using group sparsity"):
  with members:
    - "combination of hyperband and bayesian optimization for hyperparameter optimization in deep learning"
    - "parallel architecture and hyperparameter search via successive halving and classification"
    - "bohb robust and efficient hyperparameter optimization at scale"
    - "hyperparameter optimization a spectral approach"
    - "scalable gradient based tuning of continuous regularization hyperparameters"
    - "non stochastic best arm identification and hyperparameter optimization"
    - "analysis of boolean functions"
    - "auto weka combined selection and hyperparameter optimization of classification algorithms"

Connected through hyperedge HE-3 (induced by source paper "a batched scalable multi objective bayesian optimization algorithm"):
  with members:
    - "sobolev training for neural networks"
    - "neural architecture search with reinforcement learning"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "adam a method for stochastic optimization"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-4 (induced by source paper "stochastic hyperparameter optimization through hypernetworks"):
  with members:
    - "efficient hyperparameter optimization and infinitely many armed bandits"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "smash one shot model architecture search through hypernetworks"
    - "scalable gradient based tuning of continuous regularization hyperparameters"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "deep q networks for accelerating the training of deep neural networks"):
  with members:
    - "sgdr stochastic gradient descent with restarts"
    - "wide residual networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "beyond convexity stochastic quasi convex optimization"
    - "online batch selection for faster training of neural networks"
    - "train faster generalize better stability of stochastic gradient descent"
    - "adam a method for stochastic optimization"
    - "how transferable are features in deep neural networks"

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)