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 "image to video person re identification by reusing cross modal embeddings"):
  Abstract: Image-to-video person re-identification identifies a target person by a probe image from quantities of pedestrian videos captured by non-overlapping cameras. Despite the great progress achieved,it's still challenging to match in the multimodal scenario,i.e. between image and video. Currently,state-of-the-art approaches mainly focus on the task-specific data,neglecting the extra information on the different but related tasks. In this paper,we propose an end-to-end neural network framework for image-to-video person reidentification by leveraging cross-modal embeddings learned from extra information.Concretely speaking,cross-modal embeddings from image captioning and video captioning models are reused to help learned features be projected into a coordinated space,where similarity can be directly computed. Besides,training steps from fixed model reuse approach are integrated into our framework,which can incorporate beneficial information and eventually make the target networks independent of existing models. Apart from that,our proposed framework resorts to CNNs and LSTMs for extracting visual and spatiotemporal features,and combines the strengths of identification and verification model to improve the discriminative ability of the learned feature. The experimental results demonstrate the effectiveness of our framework on narrowing down the gap between heterogeneous data and obtaining observable improvement in image-to-video person re-identification.

Includes members:
  Member "deep adaptive feature embedding with local sample distributions for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "few shot deep adversarial learning for video based person re identification")
  Member "multiview spectral clustering via structured low rank matrix factorization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "attribute guided feature learning network for vehicle re identification")
  Member "where and when to look deep siamese attention networks for video based person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "pay attention to the activations a modular attention mechanism for fine grained image recognition")
  Member "effective multi query expansions collaborative deep networks for robust landmark retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discriminative feature and dictionary learning with part aware model for vehicle re identification")
  Member "beyond low rank representations orthogonal clustering basis reconstruction with optimized graph structure for multi view spectral clustering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an introduction to person re identification with generative adversarial networks")
  Member "rethinking the inception architecture for computer vision":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adversarial ranking attack and defense")
  Member "how transferable are features in deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "person re identification by local maximal occurrence representation and metric learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "weakly supervised discriminative feature learning with state information for person 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)