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: "person search in videos with one portrait through visual and temporal links"
  Abstract: In real-world applications, e.g. law enforcement and video retrieval, one often needs to search a certain person in long videos with just one portrait. This is much more challenging than the conventional settings for person re-identification, as the search may need to be carried out in the environments different from where the portrait was taken. In this paper, we aim to tackle this challenge and propose a novel framework, which takes into account the identity invariance along a tracklet, thus allowing person identities to be propagated via both the visual and the temporal links. We also develop a novel scheme called Progressive Propagation via Competitive Consensus, which significantly improves the reliability of the propagation process. To promote the study of person search, we construct a large-scale benchmark, which contains 127K manually annotated tracklets from 192 movies. Experiments show that our approach remarkably outperforms mainstream person re-id methods, raising the mAP from 42.16% to 62.27%.

Connected through hyperedge HE-1 (induced by source paper "wider face and pedestrian challenge 2018 methods and results"):
  with members:
    - "group normalization"
    - "arcface additive angular margin loss for deep face recognition"
    - "ring loss convex feature normalization for face recognition"
    - "sface an efficient network for face detection in large scale variations"
    - "unifying identification and context learning for person recognition"
    - "soft nms improving object detection with one line of code"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "face attention network an effective face detector for the occluded faces"

Connected through hyperedge HE-2 (induced by source paper "a graph based framework to bridge movies and synopses"):
  with members:
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "a joint sequence fusion model for video question answering and retrieval"
    - "from trailers to storylines an efficient way to learn from movies"
    - "multilevel language and vision integration for text to clip retrieval"
    - "unifying identification and context learning for person recognition"
    - "holistic multi modal memory network for movie question answering"
    - "dual encoding for zero example video retrieval"
    - "from benedict cumberbatch to sherlock holmes character identification in tv series without a script"

Connected through hyperedge HE-3 (induced by source paper "uncertainty modeling of contextual connection between tracklets for unconstrained video based face recognition"):
  with members:
    - "recurrent embedding aggregation network for video face recognition"
    - "video face recognition component wise feature aggregation network c fan"
    - "crystal loss and quality pooling for unconstrained face verification and recognition"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "an automatic system for unconstrained video based face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "person re identification with deep similarity guided graph neural network"

Connected through hyperedge HE-4 (induced by source paper "iqiyi vid a large dataset for multi modal person identification"):
  with members:
    - "improving person re identification by attribute and identity learning"
    - "the devil of face recognition is in the noise"
    - "arcface additive angular margin loss for deep face recognition"
    - "pyramidbox a context assisted single shot face detector"
    - "voxceleb2 deep speaker recognition"
    - "crowdhuman a benchmark for detecting human in a crowd"
    - "from benedict cumberbatch to sherlock holmes character identification in tv series without a script"
    - "video based person re identification with accumulative motion context"

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)