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 "hi cmd hierarchical cross modality disentanglement for visible infrared person re identification"):
  Abstract: Visible-infrared person re-identification (VI-ReID) is an important task in night-time surveillance applications, since visible cameras are difficult to capture valid appearance information under poor illumination conditions. Compared to traditional person re-identification that handles only the intra-modality discrepancy, VI-ReID suffers from additional cross-modality discrepancy caused by different types of imaging systems. To reduce both intra- and cross-modality discrepancies, we propose a Hierarchical Cross-Modality Disentanglement (Hi-CMD) method, which automatically disentangles ID-discriminative factors and ID-excluded factors from visible-thermal images. We only use ID-discriminative factors for robust cross-modality matching without ID-excluded factors such as pose or illumination. To implement our approach, we introduce an ID-preserving person image generation network and a hierarchical feature learning module. Our generation network learns the disentangled representation by generating a new cross-modality image with different poses and illuminations while preserving a person's identity. At the same time, the feature learning module enables our model to explicitly extract the common ID-discriminative characteristic between visible-infrared images. Extensive experimental results demonstrate that our method outperforms the state-of-the-art methods on two VI-ReID datasets.

Includes members:
  Member "progressive pose attention transfer for person image generation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi channel attention selection gans for guided image to image translation")
  Member "unsupervised person image generation with semantic parsing transformation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning to transfer texture from clothing images to 3d humans")
  Member "illumination adaptive person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "illumination adaptive person reid based on teacher student model and adversarial training")
  Member "joint discriminative and generative learning for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cross resolution adversarial dual network for person re identification and beyond")
  Member "gait recognition via disentangled representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on learning disentangled representations for gait recognition")
  Member "decorrelated adversarial learning for age invariant face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "facial attribute capsules for noise face super resolution")
  Member "re identification with consistent attentive siamese networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cross resolution adversarial dual network for person re identification and beyond")
  Member "multimodal unsupervised image to image translation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "transformation based adversarial video prediction on large scale data")

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)