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: "deep learning in the wild"
  Abstract: Deep learning with neural networks is applied by an increasing number of people outside of classic research environments, due to the vast success of the methodology on a wide range of machine perception tasks. While this interest is fueled by beautiful success stories, practical work in deep learning on novel tasks without existing baselines remains challenging. This paper explores the specific challenges arising in the realm of real world tasks, based on case studies from research \& development in conjunction with industry, and extracts lessons learned from them. It thus fills a gap between the publication of latest algorithmic and methodical developments, and the usually omitted nitty-gritty of how to make them work. Specifically, we give insight into deep learning projects on face matching, print media monitoring, industrial quality control, music scanning, strategy game playing, and automated machine learning, thereby providing best practices for deep learning in practice.

Connected through hyperedge HE-1 (induced by source paper "automated machine learning in practice state of the art and recent results"):
  with members:
    - "amc automl for model compression and acceleration on mobile devices"
    - "learning neural models for end to end clustering"
    - "large scale evolution of image classifiers"
    - "population based training of neural networks"
    - "neural networks for predicting algorithm runtime distributions"
    - "learning to learn by gradient descent by gradient descent"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "learning to optimize"

Connected through hyperedge HE-2 (induced by source paper "multimodal joint emotion and game context recognition in league of legends livestreams"):
  with members:
    - "deep unsupervised multi view detection of video game stream highlights"
    - "tensor regression networks"
    - "tensor fusion network for multimodal sentiment analysis"
    - "tensor train recurrent neural networks for video classification"
    - "end to end multimodal emotion recognition using deep neural networks"
    - "tensorizing neural networks"
    - "deep residual learning for image recognition"
    - "emonets multimodal deep learning approaches for emotion recognition in video"

Connected through hyperedge HE-3 (induced by source paper "deepscores and deep watershed detection current state and open issues"):
  with members:
    - "deep watershed detector for music object recognition"
    - "deepscores a dataset for segmentation detection and classification of tiny objects"

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)