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 "scalable and effective deep cca via soft decorrelation"):
  Abstract: Recently the widely used multi-view learning model, Canonical Correlation Analysis (CCA) has been generalised to the non-linear setting via deep neural networks. Existing deep CCA models typically first decorrelate the feature dimensions of each view before the different views are maximally correlated in a common latent space. This feature decorrelation is achieved by enforcing an exact decorrelation constraint; these models are thus computationally expensive due to the matrix inversion or SVD operations required for exact decorrelation at each training iteration. Furthermore, the decorrelation step is often separated from the gradient descent based optimisation, resulting in sub-optimal solutions. We propose a novel deep CCA model Soft CCA to overcome these problems. Specifically, exact decorrelation is replaced by soft decorrelation via a mini-batch based Stochastic Decorrelation Loss (SDL) to be optimised jointly with the other training objectives. Extensive experiments show that the proposed soft CCA is more effective and efficient than existing deep CCA models. In addition, our SDL loss can be applied to other deep models beyond multi-view learning, and obtains superior performance compared to existing decorrelation losses.

Includes members:
  Member "a discriminatively learned cnn embedding for person reidentification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "magnifiernet towards semantic regularization and fusion for person re identification")
  Member "re ranking person re identification with k reciprocal encoding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mutual mean teaching pseudo label refinery for unsupervised domain adaptation on person re identification")
  Member "person re identification by deep joint learning of multi loss classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cross resolution adversarial dual network for person re identification and beyond")
  Member "hydraplus net attentive deep features for pedestrian analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "magnifiernet towards semantic regularization and fusion for person re identification")
  Member "end to end comparative attention networks for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "disassembling the dataset a camera alignment mechanism for multiple tasks in person re identification")
  Member "unlabeled samples generated by gan improve the person re identification baseline in vitro":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "weakly supervised discriminative feature learning with state information for person identification")
  Member "learning deep context aware features over body and latent parts for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning shape representations for clothing variations in person re identification")
  Member "svdnet for pedestrian retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "cross resolution adversarial dual network for person re identification and beyond")

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)