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: "lvreid person re identification with long sequence videos"
  Abstract: This paper mainly establishes a large-scale Long sequence Video database for person re-IDentification (LVreID). Different from existing datasets, LVreID presents many important new features. (1) long sequences: the average sequence length is 200 frames, which convey more abundant cues like pose and viewpoint changes that can be explored for feature learning. (2) complex lighting, scene, and background variations: it is captured by 15 cameras located in both indoor and outdoor scenes in 12 time slots. (3) currently the largest size: it contains 3,772 identities and about 3 million bounding boxes. Those unique features in LVreID define a more challenging and realistic person ReID task. Spatial Aligned Temporal Pyramid Pooling (SATPP) network is proposed as a baseline algorithm to leverage the rich visual-temporal cues in LVreID for feature learning. SATPP jointly handles the misalignment issues in detected bounding boxes and efficiently aggregates the discriminative cues embedded in sequential video frames. Extensive experiments show feature extracted by SATPP outperforms several widely used video features. Our experiments also prove the ReID accuracy increases substantially along with longer sequence length. This demonstrates the advantage and necessity of using longer video sequences for person ReID.

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)