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 "coarse to fine a rnn based hierarchical attention model for vehicle re identification"):
  Abstract: Vehicle re-identification is an important problem and becomes desirable with the rapid expansion of applications in video surveillance and intelligent transportation. By recalling the identification process of human vision, we are aware that there exists a native hierarchical dependency when humans identify different vehicles. Specifically, humans always firstly determine one vehicle's coarse-grained category, i.e., the car model/type. Then, under the branch of the predicted car model/type, they are going to identify specific vehicles by relying on subtle visual cues, e.g., customized paintings and windshield stickers, at the fine-grained level. Inspired by the coarse-to-fine hierarchical process, we propose an end-to-end RNN-based Hierarchical Attention (RNN-HA) classification model for vehicle re-identification. RNN-HA consists of three mutually coupled modules: the first module generates image representations for vehicle images, the second hierarchical module models the aforementioned hierarchical dependent relationship, and the last attention module focuses on capturing the subtle visual information distinguishing specific vehicles from each other. By conducting comprehensive experiments on two vehicle re-identification benchmark datasets VeRi and VehicleID, we demonstrate that the proposed model achieves superior performance over state-of-the-art methods.

Includes members:
  Member "learning deep neural networks for vehicle re id with visual spatio temporal path proposals":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cross resolution adversarial dual network for person re identification and beyond")
  Member "ssd single shot multibox detector":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "phi lstm a phrase based hierarchical lstm model for image captioning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "comic toward a compact image captioning model with attention")
  Member "ask me anything free form visual question answering based on knowledge from external sources":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning to respond with stickers a framework of unifying multi modality in multi turn dialog")
  Member "a large scale car dataset for fine grained categorization and verification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "blockgan learning 3d object aware scene representations from unlabelled images")
  Member "what value do explicit high level concepts have in vision to language problems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "aitpr attribute interaction tensor product representation for image caption")
  Member "cross convolutional layer pooling for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "camal context aware multi scale attention framework for lightweight visual place recognition")
  Member "deep feature learning with relative distance comparison for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "magnifiernet towards semantic regularization and fusion for person re identification")

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)