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: "hark side of deep learning from grad student descent to automated machine learning"
  Abstract: Recent advancements in machine learning research, i.e., deep learning, introduced methods that excel conventional algorithms as well as humans in several complex tasks, ranging from detection of objects in images and speech recognition to playing difficult strategic games. However, the current methodology of machine learning research and consequently, implementations of the real-world applications of such algorithms, seems to have a recurring HARKing (Hypothesizing After the Results are Known) issue. In this work, we elaborate on the algorithmic, economic and social reasons and consequences of this phenomenon. We present examples from current common practices of conducting machine learning research (e.g. avoidance of reporting negative results) and failure of generalization ability of the proposed algorithms and datasets in actual real-life usage. Furthermore, a potential future trajectory of machine learning research and development from the perspective of accountable, unbiased, ethical and privacy-aware algorithmic decision making is discussed. We would like to emphasize that with this discussion we neither claim to provide an exhaustive argumentation nor blame any specific institution or individual on the raised issues. This is simply a discussion put forth by us, insiders of the machine learning field, reflecting on us.

Connected through hyperedge HE-1 (induced by source paper "supervised domain adaptation using graph embedding"):
  with members:
    - "d sne domain adaptation using stochastic neighborhood embedding"
    - "deep visual domain adaptation a survey"
    - "do cifar 10 classifiers generalize to cifar 10"
    - "graph embedding techniques applications and performance a survey"
    - "fine tuned language models for text classification"
    - "universal language model fine tuning for text classification"
    - "unified deep supervised domain adaptation and generalization"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-2 (induced by source paper "nas evaluation is frustratingly hard"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "sharpdarts faster and more accurate differentiable architecture search"
    - "exploring randomly wired neural networks for image recognition"
    - "xnas neural architecture search with expert advice"
    - "evaluating the search phase of neural architecture search"
    - "manas multi agent neural architecture search"
    - "darts improved differentiable architecture search with early stopping"
    - "stacnas towards stable and consistent differentiable neural architecture search"

Connected through hyperedge HE-3 (induced by source paper "periodic spectral ergodicity a complexity measure for deep neural networks and neural architecture search"):
  with members:
    - "traditional and heavy tailed self regularization in neural network models"
    - "neural persistence a complexity measure for deep neural networks using algebraic topology"
    - "pytorch an imperative style high performance deep learning library"
    - "heavy tailed universality predicts trends in test accuracies for very large pre trained deep neural networks"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "resurrecting the sigmoid in deep learning through dynamical isometry theory and practice"
    - "empirical analysis of the hessian of over parametrized neural networks"
    - "deep residual learning for 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)