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 "distributed deep convolutional neural networks for the internet of things"):
  Abstract: Due to the high demand in computation and memory, deep learning solutions are mostly restricted to high-performance computing units, e.g., those present in servers, Cloud, and computing centers. In pervasive systems, e.g., those involving Internet-of-Things (IoT) technological solutions, this would require the transmission of acquired data from IoT sensors to the computing platform and wait for its output. This solution might become infeasible when remote connectivity is either unavailable or limited in bandwidth. Moreover, it introduces uncertainty in the "data production to decision making"-latency, which, in turn, might impair control loop stability if the response should be used to drive IoT actuators. #R##N#In order to support a real-time recall phase directly at the IoT level, deep learning solutions must be completely rethought having in mind the constraints on memory and computation characterizing IoT units. In this paper we focus on Convolutional Neural Networks (CNNs), a specific deep learning solution for image and video classification, and introduce a methodology aiming at distributing their computation onto the units of the IoT system. We formalize such a methodology as an optimization problem where the latency between the data-gathering phase and the subsequent decision-making one is minimized. The methodology supports multiple IoT sources of data as well as multiple CNNs in execution on the same IoT system, making it a general-purpose distributed computing platform for CNN-based applications demanding autonomy, low decision-latency, and high Quality-of-Service.

Includes members:
  Member "distributed deep neural networks over the cloud the edge and end devices":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "run time deep model multiplexing")
  Member "origami a 803 gop s w convolutional network accelerator":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pulp nn accelerating quantized neural networks on parallel ultra low power risc v processors")
  Member "yodann an ultra low power convolutional neural network accelerator based on binary weights":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "improving efficiency in neural network accelerator using operands hamming distance optimization")
  Member "deep compression compressing deep neural networks with pruning trained quantization and huffman coding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "exploiting linear structure within convolutional networks for efficient evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "how transferable are features in deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "secure and robust machine learning for healthcare a survey")

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.LG(Machine Learning)