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 ladder a reliable leaderboard for machine learning competitions"
  Abstract: The organizer of a machine learning competition faces the problem of maintaining an accurate leaderboard that faithfully represents the quality of the best submission of each competing team. What makes this estimation problem particularly challenging is its sequential and adaptive nature. As participants are allowed to repeatedly evaluate their submissions on the leaderboard, they may begin to overfit to the holdout data that supports the leaderboard. Few theoretical results give actionable advice on how to design a reliable leaderboard. Existing approaches therefore often resort to poorly understood heuristics such as limiting the bit precision of answers and the rate of re-submission. #R##N#In this work, we introduce a notion of "leaderboard accuracy" tailored to the format of a competition. We introduce a natural algorithm called "the Ladder" and demonstrate that it simultaneously supports strong theoretical guarantees in a fully adaptive model of estimation, withstands practical adversarial attacks, and achieves high utility on real submission files from an actual competition hosted by Kaggle. #R##N#Notably, we are able to sidestep a powerful recent hardness result for adaptive risk estimation that rules out algorithms such as ours under a seemingly very similar notion of accuracy. On a practical note, we provide a completely parameter-free variant of our algorithm that can be deployed in a real competition with no tuning required whatsoever.

Connected through hyperedge HE-1 (induced by source paper "do imagenet classifiers generalize to imagenet"):
  with members:
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "do better imagenet models transfer better"
    - "spatially transformed adversarial examples"
    - "semantic adversarial examples"
    - "autoaugment learning augmentation policies from data"
    - "generalisation in humans and deep neural networks"
    - "progressive neural architecture search"

Connected through hyperedge HE-2 (induced by source paper "the advantages of multiple classes for reducing overfitting from test set reuse"):
  with members:
    - "do imagenet classifiers generalize to imagenet"
    - "do cifar 10 classifiers generalize to cifar 10"
    - "generalization in adaptive data analysis and holdout reuse"
    - "algorithmic stability for adaptive data analysis"
    - "preventing false discovery in interactive data analysis is hard"
    - "imagenet large scale visual recognition challenge"
    - "preserving statistical validity in adaptive data analysis"
    - "interactive fingerprinting codes and the hardness of preventing false discovery"

Connected through hyperedge HE-3 (induced by source paper "quantitative overfitting management for human in the loop ml application development with ease ml meter"):
  with members:
    - "continuous integration of machine learning models with ease ml ci towards a rigorous yet practical treatment"
    - "eth ds3lab at semeval 2018 task 7 effectively combining recurrent and convolutional neural networks for relation classification and extraction"
    - "deep contextualized word representations"
    - "patient risk assessment and warning symptom detection using deep attention based neural networks"
    - "ease ml towards multi tenant resource sharing for machine learning workloads"
    - "snorkel rapid training data creation with weak supervision"
    - "mllib machine learning in apache spark"
    - "preventing false discovery in interactive data analysis is hard"

Connected through hyperedge HE-4 (induced by source paper "model similarity mitigates test set overuse"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "cold case the lost mnist digits"
    - "the advantages of multiple classes for reducing overfitting from test set reuse"
    - "darts differentiable architecture search"
    - "deep residual learning for image recognition"
    - "algorithmic stability for adaptive data analysis"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "optimal multiclass overfitting by sequence reconstruction from hamming queries"):
  with members:
    - "cold case the lost mnist digits"
    - "the advantages of multiple classes for reducing overfitting from test set reuse"
    - "do imagenet classifiers generalize to imagenet"
    - "do cifar 10 classifiers generalize to cifar 10"
    - "generalization in adaptive data analysis and holdout reuse"
    - "algorithmic stability for adaptive data analysis"
    - "preserving statistical validity in adaptive data analysis"
    - "playing mastermind with many colors"

Connected through hyperedge HE-6 (induced by source paper "natural analysts in adaptive data analysis"):
  with members:
    - "the limits of post selection generalization"
    - "stable recurrent models"
    - "generalization in adaptive data analysis and holdout reuse"
    - "train faster generalize better stability of stochastic gradient descent"
    - "algorithmic stability for adaptive data analysis"
    - "preventing false discovery in interactive data analysis is hard"
    - "preserving statistical validity in adaptive data analysis"

Connected through hyperedge HE-7 (induced by source paper "continuous integration of machine learning models with ease ml ci towards a rigorous yet practical treatment"):
  with members:
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-8 (induced by source paper "the everlasting database statistical validity at a fair price"):
  with members:
    - "generalization for adaptively chosen estimators via stable median"
    - "concentrated differential privacy simplifications extensions and lower bounds"
    - "generalization in adaptive data analysis and holdout reuse"
    - "on the generalization properties of differential privacy"
    - "algorithmic stability for adaptive data analysis"
    - "preventing false discovery in interactive data analysis is hard"
    - "preserving statistical validity in adaptive data analysis"
    - "interactive fingerprinting codes and the hardness of preventing false discovery"

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)