human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "illumination adaptive person re identification"
  Abstract: Most person re-identification (ReID) approaches assume that person images are captured under relatively similar illumination conditions. In reality, long-term person retrieval is common and person images are captured under different illumination conditions at different times across a day. In this situation, the performances of existing ReID models often degrade dramatically. This paper addresses the ReID problem with illumination variations and names it as {\em Illumination-Adaptive Person Re-identification (IA-ReID)}. We propose an Illumination-Identity Disentanglement (IID) network to separate different scales of illuminations apart, while preserving individuals' identity information. To demonstrate the illumination issue and to evaluate our network, we construct two large-scale simulated datasets with a wide range of illumination variations. Experimental results on the simulated datasets and real-world images demonstrate the effectiveness of the proposed framework.

Connected through hyperedge HE-1 (induced by source paper "illumination adaptive person reid based on teacher student model and adversarial training"):
  with members:
    - "progressive pose attention transfer for person image generation"
    - "joint discriminative and generative learning for person re identification"
    - "omni scale feature learning for person re identification"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "harmonious attention network for person re identification"
    - "exploring disentangled feature representation beyond face identification"
    - "in defense of the triplet loss for person re identification"
    - "re ranking person re identification with k reciprocal encoding"

Connected through hyperedge HE-2 (induced by source paper "beyond intra modality a survey of heterogeneous person re identification"):
  with members:
    - "multi level factorisation net for person re identification"
    - "harmonious attention network for person re identification"
    - "re identification with consistent attentive siamese networks"
    - "attention aware compositional network for person re identification"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "densely semantically aligned person re identification"
    - "deep spatial feature reconstruction for partial person re identification alignment free approach"
    - "identity aware textual visual matching with latent co attention"

Connected through hyperedge HE-3 (induced by source paper "hi cmd hierarchical cross modality disentanglement for visible infrared person re identification"):
  with members:
    - "progressive pose attention transfer for person image generation"
    - "unsupervised person image generation with semantic parsing transformation"
    - "joint discriminative and generative learning for person re identification"
    - "gait recognition via disentangled representation learning"
    - "decorrelated adversarial learning for age invariant face recognition"
    - "re identification with consistent attentive siamese networks"
    - "multimodal unsupervised image to image translation"
    - "finegan unsupervised hierarchical disentanglement for fine grained object generation and discovery"

Question: Please tell me which class the center node 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)