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: "measuring the intrinsic dimension of objective landscapes"
  Abstract: Many recently trained neural networks employ large numbers of parameters to achieve good performance. One may intuitively use the number of parameters required as a rough gauge of the difficulty of a problem. But how accurate are such notions? How many parameters are really needed? In this paper we attempt to answer this question by training networks not in their native parameter space, but instead in a smaller, randomly oriented subspace. We slowly increase the dimension of this subspace, note at which dimension solutions first appear, and define this to be the intrinsic dimension of the objective landscape. The approach is simple to implement, computationally tractable, and produces several suggestive conclusions. Many problems have smaller intrinsic dimensions than one might suspect, and the intrinsic dimension for a given dataset varies little across a family of models with vastly different sizes. This latter result has the profound implication that once a parameter space is large enough to solve a problem, extra parameters serve directly to increase the dimensionality of the solution manifold. Intrinsic dimension allows some quantitative comparison of problem difficulty across supervised, reinforcement, and other types of learning where we conclude, for example, that solving the inverted pendulum problem is 100 times easier than classifying digits from MNIST, and playing Atari Pong from pixels is about as hard as classifying CIFAR-10. In addition to providing new cartography of the objective landscapes wandered by parameterized models, the method is a simple technique for constructively obtaining an upper bound on the minimum description length of a solution. A byproduct of this construction is a simple approach for compressing networks, in some cases by more than 100 times.

Connected through hyperedge HE-1 (induced by source paper "improving generalization by controlling label noise information in neural network weights"):
  with members:
    - "deep self learning from noisy labels"
    - "simple and effective regularization methods for training on noisily labeled data with generalization guarantee"
    - "where is the information in a deep neural network"
    - "how does disagreement help generalization against label corruption"
    - "unsupervised label noise modeling and loss correction"
    - "meta learning without memorization"
    - "understanding and utilizing deep neural networks trained with noisy labels"
    - "meta weight net learning an explicit mapping for sample weighting"

Connected through hyperedge HE-2 (induced by source paper "differentiating the black box optimization with local generative surrogates"):
  with members:
    - "conditioning by adaptive sampling for robust design"
    - "automatic posterior transformation for likelihood free inference"
    - "high dimensional bayesian optimization via supervised dimension reduction"
    - "pyro deep universal probabilistic programming"
    - "learning to simulate"
    - "chainqueen a real time differentiable physical simulator for soft robotics"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "guided evolutionary strategies augmenting random search with surrogate gradients"

Connected through hyperedge HE-3 (induced by source paper "multi task self supervised learning for human activity detection"):
  with members:
    - "deep learning for sensor based activity recognition a survey"
    - "unsupervised representation learning by predicting image rotations"
    - "universal language model fine tuning for text classification"
    - "protecting sensory data against sensitive inferences"
    - "cooperative learning of audio and video models from self supervised synchronization"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "audio visual scene analysis with self supervised multisensory features"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"

Connected through hyperedge HE-4 (induced by source paper "weight agnostic neural networks"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "continual lifelong learning with neural networks a review"
    - "the evolved transformer"
    - "reinforcement learning for improving agent design"
    - "evaluating the search phase of neural architecture search"
    - "regularized evolution for image classifier architecture search"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"

Connected through hyperedge HE-5 (induced by source paper "structured multi hashing for model compression"):
  with members:
    - "data free quantization through weight equalization and bias correction"
    - "searching for mobilenetv3"
    - "model compression by entropy penalized reparameterization"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "non discriminative data or weak model on the relative importance of data and model resolution"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "amc automl for model compression and acceleration on mobile devices"
    - "regularized evolution for image classifier architecture search"

Connected through hyperedge HE-6 (induced by source paper "parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization"):
  with members:
    - "scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science"
    - "recovering from random pruning on the plasticity of deep convolutional neural networks"
    - "training wide residual networks for deployment using a single bit for each weight"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"
    - "network compression using correlation analysis of layer responses"
    - "data driven sparse structure selection for deep neural networks"
    - "theory of deep learning iib optimization properties of sgd"

Connected through hyperedge HE-7 (induced by source paper "can unconditional language models recover arbitrary sentences"):
  with members:
    - "pre trained language model representations for language generation"
    - "mass masked sequence to sequence pre training for language generation"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "cross lingual language model pretraining"
    - "unified language model pre training for natural language understanding and generation"
    - "a stable and effective learning strategy for trainable greedy decoding"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-8 (induced by source paper "full deep neural network training on a pruned weight budget"):
  with members:
    - "revisiting small batch training for deep neural networks"
    - "learning efficient convolutional networks through network slimming"
    - "cnvlutin2 ineffectual activation and weight free deep neural network computing"
    - "domain adaptive deep network compression"
    - "training quantized nets a deeper understanding"
    - "thinet a filter level pruning method for deep neural network compression"
    - "deep learning with low precision by half wave gaussian quantization"
    - "incremental network quantization towards lossless cnns with low precision weights"

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)