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: "dataset distillation"
  Abstract: Model distillation aims to distill the knowledge of a complex model into a simpler one. In this paper, we consider an alternative formulation called dataset distillation: we keep the model fixed and instead attempt to distill the knowledge from a large training dataset into a small one. The idea is to synthesize a small number of data points that do not need to come from the correct data distribution, but will, when given to the learning algorithm as training data, approximate the model trained on the original data. For example, we show that it is possible to compress 60,000 MNIST training images into just 10 synthetic distilled images (one per class) and achieve close to original performance with only a few gradient descent steps, given a fixed network initialization. We evaluate our method in various initialization settings and with different learning objectives. Experiments on multiple datasets show the advantage of our approach compared to alternative methods.

Connected through hyperedge HE-1 (induced by source paper "adversarial filters of dataset biases"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "inoculation by fine tuning a method for analyzing challenge datasets"
    - "repair removing representation bias by dataset resampling"
    - "stochastic beams and where to find them the gumbel top k trick for sampling sequences without replacement"
    - "natural adversarial examples"
    - "randaugment practical automated data augmentation with a reduced search space"
    - "randaugment practical data augmentation with no separate search"

Connected through hyperedge HE-2 (induced by source paper "progressive compressed records taking a byte out of deep learning data"):
  with members:
    - "discrepancy coresets and sketches in machine learning"
    - "3lc lightweight and effective traffic compression for distributed machine learning"
    - "yet another accelerated sgd resnet 50 training on imagenet in 74 7 seconds"
    - "beyond the memory wall a case for memory centric hpc system for deep learning"
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "image classification at supercomputer scale"

Connected through hyperedge HE-3 (induced by source paper "optimizing millions of hyperparameters by implicit differentiation"):
  with members:
    - "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"
    - "on first order meta learning algorithms"

Connected through hyperedge HE-4 (induced by source paper "using hindsight to anchor past knowledge in continual learning"):
  with members:
    - "continual learning with tiny episodic memories"
    - "on first order meta learning algorithms"
    - "task free continual learning"
    - "reptile a scalable metalearning algorithm"
    - "modular meta learning"
    - "experience replay for continual learning"
    - "efficient lifelong learning with a gem"
    - "reinforced continual learning"

Connected through hyperedge HE-5 (induced by source paper "zero shot knowledge transfer via adversarial belief matching"):
  with members:
    - "variational information distillation for knowledge transfer"
    - "zero shot knowledge distillation in deep networks"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "knowledge distillation from few samples"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "data free knowledge distillation for deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "shapley homology topological analysis of sample influence for neural networks"):
  with members:
    - "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"
    - "a unified approach to interpreting model predictions"

Connected through hyperedge HE-7 (induced by source paper "deep network as memory space complexity generalization disentangled representation and interpretability"):
  with members:
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "weight agnostic neural networks"
    - "geometrization of deep networks for the interpretability of deep learning systems"
    - "understanding over parameterized deep networks by geometrization"
    - "gauge theory and twins paradox of disentangled representations"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "gan dissection visualizing and understanding generative adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "bias aware heapified policy for active learning"):
  with members:
    - "active adversarial domain adaptation"
    - "adversarial active learning for deep networks a margin based approach"
    - "meta learning transferable active learning policies by deep reinforcement learning"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "leveraging motion priors in videos for improving human segmentation"
    - "discriminative active learning"
    - "learning how to active learn a deep reinforcement learning approach"
    - "learning active learning from data"

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)