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: "query guided end to end person search"
  Abstract: Person search has recently gained attention as the novel task of finding a person, provided as a cropped sample, from a gallery of non-cropped images, whereby several other people are also visible. We believe that i. person detection and re-identification should be pursued in a joint optimization framework and that ii. the person search should leverage the query image extensively (e.g. emphasizing unique query patterns). However, so far, no prior art realizes this. We introduce a novel query-guided end-to-end person search network (QEEPS) to address both aspects. We leverage a most recent joint detector and re-identification work, OIM [37]. We extend this with i. a query-guided Siamese squeeze-and-excitation network (QSSE-Net) that uses global context from both the query and gallery images, ii. a query-guided region proposal network (QRPN) to produce query-relevant proposals, and iii. a query-guided similarity subnetwork (QSimNet), to learn a query-guided reidentification score. QEEPS is the first end-to-end query-guided detection and re-id network. On both the most recent CUHK-SYSU [37] and PRW [46] datasets, we outperform the previous state-of-the-art by a large margin.

Connected through hyperedge HE-1 (induced by source paper "triplet online instance matching loss for person re identification"):
  with members:
    - "multi pseudo regularized label for generated data in person re identification"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "multi level factorisation net for person re identification"
    - "person search via a mask guided two stream cnn model"
    - "pyramidal person re identification via multi loss dynamic training"
    - "neural person search machines"
    - "pose normalized image generation for person re identification"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"

Connected through hyperedge HE-2 (induced by source paper "knowledge distillation for end to end person search"):
  with members:
    - "learning context graph for person search"
    - "person search by multi scale matching"
    - "model compression via distillation and quantization"
    - "attention aware compositional network for person re identification"
    - "person re identification with deep similarity guided graph neural network"
    - "maximum margin metric learning over discriminative nullspace for person re identification"
    - "person search via a mask guided two stream cnn model"
    - "knowledge distillation in generations more tolerant teachers educate better students"

Connected through hyperedge HE-3 (induced by source paper "segmentation mask guided end to end person search"):
  with members:
    - "learning context graph for person search"
    - "aanet attribute attention network for person re identifications"
    - "diversity regularized spatiotemporal attention for video based person re identification"
    - "in defense of the triplet loss for person re identification"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "neural person search machines"
    - "end to end comparative attention networks for person re identification"
    - "ian the individual aggregation network for person search"

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)