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 "vehicle re identification exploring feature fusion using multi stream convolutional networks"):
  Abstract: This work addresses the problem of vehicle re-identification through a network of non-overlapping cameras. As our main contribution, we propose a novel two-stream convolutional neural network (CNN) that simultaneously uses two of the most distinctive and persistent features available: the vehicle appearance and its license plate. This is an attempt to tackle a major problem, false alarms caused by vehicles with similar design or by very close license plate identifiers. In the first network stream, shape similarities are identified by a Siamese CNN that uses a pair of low-resolution vehicle patches recorded by two different cameras. In the second stream, we use a CNN for optical character recognition (OCR) to extract textual information, confidence scores, and string similarities from a pair of high-resolution license plate patches. Then, features from both streams are merged by a sequence of fully connected layers for decision. As part of this work, we created an important dataset for vehicle re-identification with more than three hours of videos spanning almost 3,000 vehicles. In our experiments, we achieved a precision, recall and F -score values of 99.6%, 99.2% and 99.4%, respectively. As another contribution, we discuss and compare three alternative architectures that explore the same features but using additional streams and temporal information. The proposed architectures, trained models, and dataset are publicly available at this https URL .

Includes members:
  Member "an efficient and layout independent automatic license plate recognition system based on the yolo detector":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "can giraffes become birds an evaluation of image to image translation for data generation")
  Member "a robust real time automatic license plate recognition based on the yolo detector":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep representations for cross spectral ocular biometrics")
  Member "license plate detection and recognition using deeply learned convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "spot evasion attacks adversarial examples for license plate recognition systems with convolution neural networks")
  Member "learning deep neural networks for vehicle re id with visual spatio temporal path proposals":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cross resolution adversarial dual network for person re identification and beyond")
  Member "learning contextual dependence with convolutional hierarchical recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scene recognition with prototype agnostic scene layout")
  Member "you only look once unified real time object detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "evaluating two stream cnn for video classification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "probabilistic semantic retrieval for surveillance videos with activity graphs")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

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)