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: "a first look at deep learning apps on smartphones"
  Abstract: We are in the dawn of deep learning explosion for smartphones. To bridge the gap between research and practice, we present the first empirical study on 16,500 the most popular Android apps, demystifying how smartphone apps exploit deep learning in the wild. To this end, we build a new static tool that dissects apps and analyzes their deep learning functions. Our study answers threefold questions: what are the early adopter apps of deep learning, what do they use deep learning for, and how do their deep learning models look like. Our study has strong implications for app developers, smartphone vendors, and deep learning R\&D. On one hand, our findings paint a promising picture of deep learning for smartphones, showing the prosperity of mobile deep learning frameworks as well as the prosperity of apps building their cores atop deep learning. On the other hand, our findings urge optimizations on deep learning models deployed on smartphones, the protection of these models, and validation of research ideas on these models.

Connected through hyperedge HE-1 (induced by source paper "neural architecture search over decentralized data"):
  with members:
    - "towards federated learning at scale system design"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "deepcache principled cache for mobile deep vision"
    - "federated learning strategies for improving communication efficiency"
    - "leaf a benchmark for federated settings"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "path level network transformation for efficient architecture search"
    - "federated learning for mobile keyboard prediction"

Connected through hyperedge HE-2 (induced by source paper "mind your weight s a large scale study on insufficient machine learning model protection in mobile apps"):
  with members:
    - "high fidelity extraction of neural network models"
    - "on device neural net inference with mobile gpus"
    - "tensorscone a secure tensorflow framework using intel sgx"
    - "knockoff nets stealing functionality of black box models"
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"
    - "csi neural network using side channels to recover your artificial neural network information"
    - "privado practical and secure dnn inference"
    - "stealing hyperparameters in machine learning"

Connected through hyperedge HE-3 (induced by source paper "characterizing the deep neural networks inference performance of mobile applications"):
  with members:
    - "no dnn left behind improving inference in the cloud with multi tenancy"
    - "deepcache principled cache for mobile deep vision"
    - "nestdnn resource aware multi tenant on device deep learning for continuous mobile vision"
    - "serving deep learning models in a serverless platform"
    - "learning transferable architectures for scalable image recognition"
    - "tensorflow serving flexible high performance ml serving"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-4 (induced by source paper "an empirical study towards characterizing deep learning development and deployment across different frameworks and platforms"):
  with members:
    - "moving deep learning into web browser how far can we go"
    - "deep learning towards mobile applications"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "deepmutation mutation testing of deep learning systems"
    - "lstm a search space odyssey"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "comparative analysis of open source frameworks for machine learning with use case in single threaded and multi threaded modes"

Connected through hyperedge HE-5 (induced by source paper "ten ai stepping stones for cybersecurity"):
  with members:
    - "machine learning ddos detection for consumer internet of things devices"
    - "adversarial deep learning for robust detection of binary encoded malware"
    - "cache telepathy leveraging shared resource attacks to learn dnn architectures"
    - "provable limitations of deep learning"
    - "adversarial malware binaries evading deep learning for malware detection in executables"
    - "prada protecting against dnn model stealing attacks"
    - "logan membership inference attacks against generative models"
    - "malicious url detection using machine learning a survey"

Connected through hyperedge HE-6 (induced by source paper "dabnn a super fast inference framework for binary neural networks on arm devices"):
  with members:
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "bmxnet an open source binary neural network implementation based on mxnet"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "caffe convolutional architecture for fast feature embedding"

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)