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 towards mobile applications"
  Abstract: Recent years have witnessed an explosive growth of mobile devices. Mobile devices are permeating every aspect of our daily lives. With the increasing usage of mobile devices and intelligent applications, there is a soaring demand for mobile applications with machine learning services. Inspired by the tremendous success achieved by deep learning in many machine learning tasks, it becomes a natural trend to push deep learning towards mobile applications. However, there exist many challenges to realize deep learning in mobile applications, including the contradiction between the miniature nature of mobile devices and the resource requirement of deep neural networks, the privacy and security concerns about individuals' data, and so on. To resolve these challenges, during the past few years, great leaps have been made in this area. In this paper, we provide an overview of the current challenges and representative achievements about pushing deep learning on mobile devices from three aspects: training with mobile data, efficient inference on mobile devices, and applications of mobile deep learning. The former two aspects cover the primary tasks of deep learning. Then, we go through our two recent applications that apply the data collected by mobile devices to inferring mood disturbance and user identification. Finally, we conclude this paper with the discussion of the future of this area.

Connected through hyperedge HE-1 (induced by source paper "mdldroid a chainsgd reduce approach to mobile deep learning for personal mobile sensing"):
  with members:
    - "a distributed synchronous sgd algorithm with global top k sparsification for low bandwidth networks"
    - "deep learning on mobile devices a review"
    - "layered sgd a decentralized and synchronous sgd algorithm for scalable deep neural network training"
    - "towards federated learning at scale system design"
    - "multi modality sensor data classification with selective attention"
    - "analyzing federated learning through an adversarial lens"
    - "edge intelligence on demand deep learning model co inference with device edge synergy"
    - "deep reinforcement learning for multi agent systems a review of challenges solutions and applications"

Connected through hyperedge HE-2 (induced by source paper "self binarizing networks"):
  with members:
    - "effnet an efficient structure for convolutional neural networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "towards accurate binary convolutional neural network"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "hashnet deep learning to hash by continuation"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "gxnor net training deep neural networks with ternary weights and activations without full precision memory under a unified discretization framework"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"

Connected through hyperedge HE-3 (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"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "deepmutation mutation testing of deep learning systems"
    - "a first look at deep learning apps on smartphones"
    - "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-4 (induced by source paper "jsdoop and tensorflow js volunteer distributed web browser based neural network training"):
  with members:
    - "tensorflow js machine learning for the web and beyond"
    - "towards federated learning at scale system design"
    - "moving deep learning into web browser how far can we go"
    - "boinc a platform for volunteer computing"
    - "deep learning in mobile and wireless networking a survey"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "sparse communication for distributed gradient descent"
    - "distributed deep neural networks over the cloud the edge and end devices"

Connected through hyperedge HE-5 (induced by source paper "an orchestrated empirical study on deep learning frameworks and platforms"):
  with members:
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "deepmutation mutation testing of deep learning systems"
    - "combinatorial testing for deep learning systems"
    - "deep learning with dynamic computation graphs"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"
    - "comparative analysis of open source frameworks for machine learning with use case in single threaded and multi threaded modes"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "benchmarking state of the art deep learning software tools"

Connected through hyperedge HE-6 (induced by source paper "effective fast and memory efficient compressed multi function convolutional neural networks for more accurate medical image classification"):
  with members:
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"

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)