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: "survey on deep learning techniques for person re identification task"
  Abstract: Intelligent video-surveillance is currently an active research field in computer vision and machine learning techniques. It provides useful tools for surveillance operators and forensic video investigators. Person re-identification (PReID) is one among these tools. It consists of recognizing whether an individual has already been observed over a camera in a network or not. This tool can also be employed in various possible applications such as off-line retrieval of all the video-sequences showing an individual of interest whose image is given a query, and online pedestrian tracking over multiple camera views. To this aim, many techniques have been proposed to increase the performance of PReID. Among the systems, many researchers utilized deep neural networks (DNNs) because of their better performance and fast execution at test time. Our objective is to provide for future researchers the work being done on PReID to date. Therefore, we summarized state-of-the-art DNN models being used for this task. A brief description of each model along with their evaluation on a set of benchmark datasets is given. Finally, a detailed comparison is provided among these models followed by some limitations that can work as guidelines for future research.

Connected through hyperedge HE-1 (induced by source paper "learning landmark guided embeddings for animal re identification"):
  with members:
    - "robust re identification of manta rays from natural markings by learning pose invariant embeddings"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "amur tiger re identification in the wild"
    - "spatial temporal person re identification"
    - "aligned to the object not to the image a unified pose aligned representation for fine grained recognition"
    - "in defense of the triplet loss for person re identification"
    - "a pose sensitive embedding for person re identification with expanded cross neighborhood re ranking"
    - "pose driven deep convolutional model for person re identification"

Connected through hyperedge HE-2 (induced by source paper "relation aware global attention"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "residual non local attention networks for image restoration"
    - "learning discriminative features with multiple granularities for person re identification"
    - "ocnet object context network for scene parsing"
    - "maskreid a mask based deep ranking neural network for person re identification"
    - "horizontal pyramid matching for person re identification"
    - "dual attention matching network for context aware feature sequence based person re identification"
    - "recognizing partial biometric patterns"

Connected through hyperedge HE-3 (induced by source paper "running event visualization using videos from multiple cameras"):
  with members:
    - "bag of tricks and a strong baseline for deep person re identification"
    - "fots fast oriented text spotting with a unified network"
    - "an end to end textspotter with explicit alignment and attention"
    - "scene text detection and recognition the deep learning era"
    - "mask textspotter an end to end trainable neural network for spotting text with arbitrary shapes"
    - "in defense of the triplet loss for person re identification"
    - "east an efficient and accurate scene text detector"
    - "instance sensitive fully convolutional networks"

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)