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 "on learning disentangled representations for gait recognition"):
  Abstract: Gait, the walking pattern of individuals, is one of the important biometrics modalities. Most of the existing gait recognition methods take silhouettes or articulated body models as gait features. These methods suffer from degraded recognition performance when handling confounding variables, such as clothing, carrying and viewing angle. To remedy this issue, we propose a novel AutoEncoder framework, GaitNet, to explicitly disentangle appearance, canonical and pose features from RGB imagery. The LSTM integrates pose features over time as a dynamic gait feature while canonical features are averaged as a static gait feature. Both of them are utilized as classification features. In addition, we collect a Frontal-View Gait (FVG) dataset to focus on gait recognition from frontal-view walking, which is a challenging problem since it contains minimal gait cues compared to other views. FVG also includes other important variations, e.g., walking speed, carrying, and clothing. With extensive experiments on CASIA-B, USF, and FVG datasets, our method demonstrates superior performance to the SOTA quantitatively, the ability of feature disentanglement qualitatively, and promising computational efficiency. We further compare our GaitNet with state-of-the-art face recognition to demonstrate the advantages of gait biometrics identification under certain scenarios, e.g., long distance/lower resolutions, cross viewing angles.

Includes members:
  Member "representation learning by rotating your faces":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "rotate and render unsupervised photorealistic face rotation from single view images")
  Member "on learning 3d face morphable model from in the wild images":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "retinaface single stage dense face localisation in the wild")
  Member "towards high fidelity nonlinear 3d face morphable model":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "towards a complete 3d morphable model of the human head")
  Member "gait recognition via disentangled representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hi cmd hierarchical cross modality disentanglement for visible infrared person re identification")
  Member "retinaface single stage dense face localisation in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generalizing face representation with unlabeled data")
  Member "synthesizing images of humans in unseen poses":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "neural human video rendering joint learning of dynamic textures and rendering to video translation")
  Member "towards highly accurate and stable face alignment for high resolution videos":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "anti confusing region aware network for human pose estimation")
  Member "disentangling features in 3d face shapes for joint face reconstruction and recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "transferable recognition aware image processing")

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)