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 advantages of multiple classes for reducing overfitting from test set reuse"
  Abstract: Excessive reuse of holdout data can lead to overfitting. However, there is little concrete evidence of significant overfitting due to holdout reuse in popular multiclass benchmarks today. Known results show that, in the worst-case, revealing the accuracy of $k$ adaptively chosen classifiers on a data set of size $n$ allows to create a classifier with bias of $\Theta(\sqrt{k/n})$ for any binary prediction problem. We show a new upper bound of $\tilde O(\max\{\sqrt{k\log(n)/(mn)},k/n\})$ on the worst-case bias that any attack can achieve in a prediction problem with $m$ classes. Moreover, we present an efficient attack that achieve a bias of $\Omega(\sqrt{k/(m^2 n)})$ and improves on previous work for the binary setting ($m=2$). We also present an inefficient attack that achieves a bias of $\tilde\Omega(k/n)$. Complementing our theoretical work, we give new practical attacks to stress-test multiclass benchmarks by aiming to create as large a bias as possible with a given number of queries. Our experiments show that the additional uncertainty of prediction with a large number of classes indeed mitigates the effect of our best attacks. #R##N#Our work extends developments in understanding overfitting due to adaptive data analysis to multiclass prediction problems. It also bears out the surprising fact that multiclass prediction problems are significantly more robust to overfitting when reusing a test (or holdout) dataset. This offers an explanation as to why popular multiclass prediction benchmarks, such as ImageNet, may enjoy a longer lifespan than what intuition from literature on binary classification suggests.

Connected through hyperedge HE-1 (induced by source paper "mdea malware detection with evolutionary adversarial learning"):
  with members:
    - "learning to evade static pe machine learning malware models via reinforcement learning"
    - "regularized evolution for image classifier architecture search"
    - "evolutionary generative adversarial networks"
    - "adversarial malware binaries evading deep learning for malware detection in executables"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"

Connected through hyperedge HE-2 (induced by source paper "a new analysis of differential privacy s generalization guarantees"):
  with members:
    - "a necessary and sufficient stability notion for adaptive generalization"
    - "guaranteed validity for empirical approaches to adaptive data analysis"
    - "mitigating bias in adaptive data gathering via differential privacy"
    - "generalization bounds for uniformly stable algorithms"
    - "generalization for adaptively chosen estimators via stable median"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "calibrating noise to variance in adaptive data analysis"
    - "subgaussian tail bounds via stability arguments"

Connected through hyperedge HE-3 (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"
    - "darts differentiable architecture search"
    - "the ladder a reliable leaderboard for machine learning competitions"
    - "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-4 (induced by source paper "optimal multiclass overfitting by sequence reconstruction from hamming queries"):
  with members:
    - "cold case the lost mnist digits"
    - "do imagenet classifiers generalize to imagenet"
    - "do cifar 10 classifiers generalize to cifar 10"
    - "the ladder a reliable leaderboard for machine learning competitions"
    - "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-5 (induced by source paper "cold case the lost mnist digits"):
  with members:
    - "do imagenet classifiers generalize to imagenet"
    - "do cifar 10 classifiers generalize to cifar 10"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"

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)