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 "disassembling the dataset a camera alignment mechanism for multiple tasks in person re identification"):
  Abstract: In person re-identification (ReID), one of the main challenges is the distribution inconsistency among different datasets. Previous researchers have defined several seemingly individual topics, such as fully supervised learning, direct transfer, domain adaptation, and incremental learning, each with different settings of training and testing scenarios. These topics are designed in a dataset-wise manner, i.e., images from the same dataset, even from disjoint cameras, are presumed to follow the same distribution. However, such distribution is coarse and training-set-specific, and the ReID knowledge learned in such manner works well only on the corresponding scenarios. To address this issue, we propose a fine-grained distribution alignment formulation, which disassembles the dataset and aligns all training and testing cameras. It connects all topics above and guarantees that ReID knowledge is always learned, accumulated, and verified in the aligned distributions. In practice, we devise the Camera-based Batch Normalization, which is easy for integration and nearly cost-free for existing ReID methods. Extensive experiments on the above four ReID tasks demonstrate the superiority of our approach. The code will be publicly available.

Includes members:
  Member "view confusion feature learning for person re identification":
    also connected through other hyperedges:
      (none)
  Member "resolution invariant person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "refinements in motion and appearance for online multi object tracking")
  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 "attention a big surprise for cross domain person re identification")
  Member "multi level factorisation net for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cross resolution adversarial dual network for person re identification and beyond")
  Member "harmonious attention network for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "cross resolution adversarial dual network for person re identification and beyond")
  Member "maskreid a mask based deep ranking neural network for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "high order information matters learning relation and topology for occluded 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)