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 "a scene perception system for visually impaired based on object detection and classification using multi modal dcnn"):
  Abstract: This paper represents a cost-effective scene perception system aimed towards visually impaired individual. We use an odroid system integrated with an USB camera and USB laser that can be attached on the chest. The system classifies the detected objects along with its distance from the user and provides a voice output. Experimental results provided in this paper use outdoor traffic scenes. The object detection and classification framework exploits a multi-modal fusion based faster RCNN using motion, sharpening and blurring filters for efficient feature representation.

Includes members:
  Member "noscope optimizing neural network queries over video at scale":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "video monitoring queries")
  Member "deep feature based contextual model for object detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "object detection with convolutional neural networks")
  Member "brain inspired deep networks for image aesthetics assessment":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep cross networks with aesthetic preference for cross domain recommendation")
  Member "attentive contexts for object detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "eleatt rnn adding attentiveness to neurons in recurrent neural networks")
  Member "multispectral deep neural networks for pedestrian detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "box level segmentation supervised deep neural networks for accurate and real time multispectral pedestrian detection")
  Member "fast r cnn":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")

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.CV(Computer Vision and Pattern Recognition)