human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "moving deep learning into web browser how far can we go"):
  Abstract: Recently, several JavaScript-based deep learning frameworks have emerged, making it possible to perform deep learning tasks directly in browsers. However, little is known on what and how well we can do with these frameworks for deep learning in browsers. To bridge the knowledge gap, in this paper, we conduct the first empirical study of deep learning in browsers. We survey 7 most popular JavaScript-based deep learning frameworks, investigating to what extent deep learning tasks have been supported in browsers so far. Then we measure the performance of different frameworks when running different deep learning tasks. Finally, we dig out the performance gap between deep learning in browsers and on native platforms by comparing the performance of TensorFlow.js and TensorFlow in Python. Our findings could help application developers, deep-learning framework vendors and browser vendors to improve the efficiency of deep learning in browsers.

Includes members:
  Member "inverse reinforcement learning for video games":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "efficiently guiding imitation learning algorithms with human gaze")
  Member "ai benchmark running deep neural networks on android smartphones":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "replacing mobile camera isp with a single deep learning model")
  Member "not just privacy improving performance of private deep learning in mobile cloud":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "neural architecture search over decentralized data")
  Member "the need for speed of ai applications performance comparison of native vs browser based algorithm implementations":
    also connected through other hyperedges:
      (none)
  Member "performance modeling and evaluation of distributed deep learning frameworks on gpus":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "communication contention aware scheduling of multiple deep learning training jobs")
  Member "distributed deep neural networks over the cloud the edge and end devices":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "run time deep model multiplexing")
  Member "deepsense a unified deep learning framework for time series mobile sensing data processing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "patdnn achieving real time dnn execution on mobile devices with pattern based weight pruning")
  Member "tensorflow a system for large scale machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

Question: Please tell me which class the source paper 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.SE(Software Engineering)