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 "frustratingly easy person re identification generalizing person re id in practice"):
  Abstract: Contemporary person re-identification (\reid) methods usually require access to data from the deployment camera network during training in order to perform well. This is because contemporary \reid{} models trained on one dataset do not generalise to other camera networks due to the domain-shift between datasets. This requirement is often the bottleneck for deploying \reid{} systems in practical security or commercial applications, as it may be impossible to collect this data in advance or prohibitively costly to annotate it. This paper alleviates this issue by proposing a simple baseline for domain generalizable~(DG) person re-identification. That is, to learn a \reid{} model from a set of source domains that is suitable for application to unseen datasets out-of-the-box, without any model updating. Specifically, we observe that the domain discrepancy in \reid{} is due to style and content variance across datasets and demonstrate appropriate Instance and Feature Normalization alleviates much of the resulting domain-shift in Deep \reid{} models. Instance Normalization~(IN) in early layers filters out style statistic variations and Feature Normalization~(FN) in deep layers is able to further eliminate disparity in content statistics. Compared to contemporary alternatives, this approach is extremely simple to implement, while being faster to train and test, thus making it an extremely valuable baseline for implementing \reid{} in practice. With a few lines of code, it increases the rank 1 \reid{} accuracy by {11.8\%, 33.2\%, 12.8\% and 8.5\%} on the VIPeR, PRID, GRID, and i-LIDS benchmarks respectively. Source codes are available at \url{https://github.com/BJTUJia/person_reID_DualNorm}.

Includes members:
  Member "feature critic networks for heterogeneous domain generalization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "revisiting meta learning as supervised learning")
  Member "invariance matters exemplar memory for domain adaptive person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "weakly supervised discriminative feature learning with state information for person identification")
  Member "bag of tricks and a strong baseline for deep person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "building computationally efficient and well generalizing person re identification models with metric learning")
  Member "eanet enhancing alignment for cross domain person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "disassembling the dataset a camera alignment mechanism for multiple tasks in person re identification")
  Member "mobilenetv2 inverted residuals and linear bottlenecks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "multi task mid level feature alignment network for unsupervised cross dataset person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "memorizing comprehensively to learn adaptively unsupervised cross domain person re id with multi level memory")
  Member "transferable joint attribute identity deep learning for unsupervised person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "weakly supervised discriminative feature learning with state information for person identification")
  Member "batch instance normalization for adaptively style invariant neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "augmented cyclic consistency regularization for unpaired image to image translation")

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)