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 "an efficient framework for visible infrared cross modality person re identification"):
  Abstract: Visible-infrared cross modality person re-identification (VI-ReId) is an important task for video surveillance in poorly illuminated or dark environments. Despite many recent studies on person re-identification in visible domain (ReId), there are few studies dealing with VI-ReId. Besides challenges that are common for both ReId and VI-ReId such as pose/illumination variations, background clutter and occlusion, VI-ReId has additional challenges as color information is not available in infrared images. As a result, the performance of VI-ReId systems is typically lower than ReId systems. In this work, we propose a 4-stream framework to improve VI-ReId performance. We train a separate deep convolutional neural network in each stream using different representations of input images. We expect that different and complementary features can be learned from each stream. In our framework, grayscale and infrared input images are used to train the ResNet in the first stream. In the second stream, RGB and 3-channel infrared images (created by repeating infrared channel) are used. In the remaining two streams, we use local pattern maps as input images. These maps are generated utilizing local Zernike moments transformation. Local pattern maps are obtained from grayscale and infrared images in the 3rd stream and from RGB and 3-channel infrared images in the last stream. We improve the performance of the proposed framework by employing a re-ranking algorithm for post processing. Our results indicate that the proposed framework outperforms current state-of-the-art on SYSU-MM01 dataset with large margin by improving Rank-1/mAP by 34.2%/37.9% and 37.4%/34.8% under all-search and indoor-search modes, respectively.

Includes members:
  Member "cross spectral face completion for nir vis heterogeneous face recognition":
    also connected through other hyperedges:
      (none)
  Member "zernet convolutional neural networks on arbitrary surfaces via zernike local tangent space estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gauge equivariant mesh cnns anisotropic convolutions on geometric graphs")
  Member "skipnet learning dynamic routing in convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "switchable precision neural networks")
  Member "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "rotate and render unsupervised photorealistic face rotation from single view images")
  Member "adversarial discriminative heterogeneous face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "relational deep feature learning for heterogeneous face recognition")
  Member "momentsnet a simple learning free method for binary image recognition":
    also connected through other hyperedges:
      (none)
  Member "not afraid of the dark nir vis face recognition via cross spectral hallucination and low rank embedding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "effective domain knowledge transfer with soft fine tuning")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")

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)