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: "deepdetect a cascaded region based densely connected network for seismic event detection"
  Abstract: Automatic event detection from time series signals has broad applications. Traditional detection methods detect events primarily by the use of similarity and correlation in data. Those methods can be inefficient and yield low accuracy. In recent years, machine learning techniques have revolutionized many sciences and engineering domains. In particular, the performance of object detection in a 2-D image data has significantly improved due to deep neural networks. In this paper, we develop a deep-learning-based detection method, called “DeepDetect,” to detect events from seismic signals. We find that the direct adaptation of similar ideas from 2-D object detection to our problem faces two challenges. The first challenge is that the duration of earthquake event varies significantly; the other is that the proposals generated are temporally correlated. To address these challenges, we propose a novel cascaded region-based convolutional neural network to capture earthquake events in different sizes while incorporating contextual information to enrich features for each proposal. To achieve a better generalization performance, we use densely connected blocks as the backbone of our network. Because some positive events are not correctly annotated, we further formulate the detection problem as a learning-from-noise problem. To verify the performance, we employ the seismic data generated from the Pennsylvania State University Rock and Sediment Mechanics Laboratory, and we acquire labels with the help of experts. We show that our techniques yield high accuracy. Therefore, our novel deep-learning-based detection methods can potentially be powerful tools for identifying events from the time series data in various applications.

Connected through hyperedge HE-1 (induced by source paper "earthquakegen earthquake simulation using generative adversarial networks"):
  with members:
    - "gansynth adversarial neural audio synthesis"
    - "a multi discriminator cyclegan for unsupervised non parallel speech domain adaptation"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "spectral normalization for generative adversarial networks"
    - "generative adversarial networks an overview"
    - "large scale gan training for high fidelity natural image synthesis"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "began boundary equilibrium generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "adversarial example in remote sensing image recognition"):
  with members:
    - "detnet a backbone network for object detection"
    - "adversarial attacks and defences a survey"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "remote sensing image scene classification benchmark and state of the art"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "towards evaluating the robustness of neural networks"
    - "adversarial examples in the physical world"

Connected through hyperedge HE-3 (induced by source paper "phaselink a deep learning approach to seismic phase association"):
  with members:
    - "carla an open urban driving simulator"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "fine grained prediction of syntactic typology discovering latent structure with supervised learning"
    - "image captioning with semantic attention"
    - "play and learn using video games to train computer vision models"
    - "wavenet a generative model for raw audio"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "neural network applications in earthquake prediction 1994 2019 meta analytic insight on their limitations"):
  with members:
    - "phaselink a deep learning approach to seismic phase association"
    - "cred a deep residual network of convolutional and recurrent units for earthquake signal detection"
    - "deep big simple neural nets for handwritten digit recognition"

Connected through hyperedge HE-5 (induced by source paper "cred a deep residual network of convolutional and recurrent units for earthquake signal detection"):
  with members:
    - "locality sensitive hashing for earthquake detection a case study scaling data driven science"
    - "deep residual learning for image recognition"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "sequence to sequence learning with neural networks"

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)