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 "dissecting person re identification from the viewpoint of viewpoint"):
  Abstract: Variations in visual factors such as viewpoint, pose, illumination and background, are usually viewed as important challenges in person re-identification (re-ID). In spite of acknowledging these factors to be influential, quantitative studies on how they affect a re-ID system are still lacking. To derive insights in this scientific campaign, this paper makes an early attempt in studying a particular factor, viewpoint. We narrow the viewpoint problem down to the pedestrian rotation angle to obtain focused conclusions. In this regard, this paper makes two contributions to the community. First, we introduce a large-scale synthetic data engine, PersonX. Composed of hand-crafted 3D person models, the salient characteristic of this engine is "controllable". That is, we are able to synthesize pedestrians by setting the visual variables to arbitrary values. Second, on the 3D data engine, we quantitatively analyze the influence of pedestrian rotation angle on re-ID accuracy. Comprehensively, the person rotation angles are precisely customized from 0 to 360, allowing us to investigate its effect on the training, query, and gallery sets. Extensive experiment helps us have a deeper understanding of the fundamental problems in person re-ID. Our research also provides useful insights for dataset building and future practical usage, e.g., a person of a side view makes a better query.

Includes members:
  Member "person search via a mask guided two stream cnn model":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "triplet online instance matching loss for person re identification")
  Member "domain adaptation through synthesis for unsupervised person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "memorizing comprehensively to learn adaptively unsupervised cross domain person re id with multi level memory")
  Member "resource aware person re identification across multiple resolutions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning shape representations for clothing variations in person re identification")
  Member "in defense of the triplet loss for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")
  Member "a pose sensitive embedding for person re identification with expanded cross neighborhood re ranking":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "diversity achieving slow dropblock network for person re identification")
  Member "looking beyond appearances synthetic training data for deep cnns in re identification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning multi human optical flow")
  Member "learning from synthetic data addressing domain shift for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "crdoco pixel level domain transfer with cross domain consistency")
  Member "pose driven deep convolutional model for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "memorizing comprehensively to learn adaptively unsupervised cross domain person re id with multi level memory")

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)