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: "deepn jpeg a deep neural network favorable jpeg based image compression framework"
  Abstract: As one of most fascinating machine learning techniques, deep neural network (DNN) has demonstrated excellent performance in various intelligent tasks such as image classification. DNN achieves such performance, to a large extent, by performing expensive training over huge volumes of training data. To reduce the data storage and transfer overhead in smart resource-limited Internet-of-Thing (IoT) systems, effective data compression is a "must-have" feature before transferring real-time produced dataset for training or classification. While there have been many well-known image compression approaches (such as JPEG), we for the first time find that a human-visual based image compression approach such as JPEG compression is not an optimized solution for DNN systems, especially with high compression ratios. To this end, we develop an image compression framework tailored for DNN applications, named "DeepN-JPEG", to embrace the nature of deep cascaded information process mechanism of DNN architecture. Extensive experiments, based on "ImageNet" dataset with various state-of-the-art DNNs, show that "DeepN-JPEG" can achieve ~3.5x higher compression rate over the popular JPEG solution while maintaining the same accuracy level for image recognition, demonstrating its great potential of storage and power efficiency in DNN-based smart IoT system design.

Connected through hyperedge HE-1 (induced by source paper "optimizing jpeg quantization for classification networks"):
  with members:
    - "do imagenet classifiers generalize to imagenet"
    - "understanding how image quality affects deep neural networks"
    - "deep residual learning for image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-2 (induced by source paper "measurement driven analysis of an edge assisted object recognition system"):
  with members:
    - "yolov3 an incremental improvement"
    - "jalad joint accuracy and latency aware deep structure decoupling for edge cloud execution"
    - "distributed deep neural networks over the cloud the edge and end devices"
    - "understanding how image quality affects deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "adacompress adaptive compression for online computer vision services"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "jalad joint accuracy and latency aware deep structure decoupling for edge cloud execution"
    - "towards image understanding from deep compression without decoding"
    - "robust physical world attacks on deep learning models"
    - "borrowing treasures from the wealthy deep transfer learning through selective joint fine tuning"
    - "full resolution image compression with recurrent neural networks"
    - "understanding how image quality affects deep neural networks"

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.CV(Computer Vision and Pattern Recognition)