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: "dyvedeep dynamic variable effort deep neural networks"
  Abstract: Deep Neural Networks (DNNs) have advanced the state-of-the-art on a variety of machine learning tasks and are deployed widely in many real-world products. However, the compute and data requirements demanded by large-scale DNNs remains a significant challenge. In this work, we address this challenge in the context of DNN inference. We propose Dynamic Variable Effort Deep Neural Networks (DyVEDeep), which exploit the heterogeneity in the characteristics of inputs to DNNs to improve their compute efficiency while maintaining the same classification accuracy. DyVEDeep equips DNNs with dynamic effort knobs, which in course of processing an input, identify how critical a group of computations are to classify the input. DyVEDeep dynamically focuses its compute effort only on the critical computations, while the skipping/approximating the rest. We propose 3 effort knobs that operate at different levels of granularity viz. neuron, feature and layer levels. We build DyVEDeep versions for 5 popular image recognition benchmarks on 3 image datasets---MNIST, CIFAR and ImageNet. Across all benchmarks, DyVEDeep achieves 2.1X-2.6X reduction in number of scalar operations, which translates to 1.9X-2.3X performance improvement over a Caffe-based sequential software implementation, for negligible loss in accuracy.

Connected through hyperedge HE-1 (induced by source paper "pack and detect fast object detection in videos using region of interest packing"):
  with members:
    - "yolov3 an incremental improvement"
    - "recovering from random pruning on the plasticity of deep convolutional neural networks"
    - "recurrent residual module for fast inference in videos"
    - "object detection in videos by short and long range object linking"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "fast yolo a fast you only look once system for real time embedded object detection in video"
    - "dynamic deep neural networks optimizing accuracy efficiency trade offs by selective execution"
    - "towards high performance video object detection"

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.NE(Neural and Evolutionary Computing)