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: "detecting temporally consistent objects in videos through object class label propagation"
  Abstract: Object proposals for detecting moving or static video objects need to address issues such as speed, memory complexity and temporal consistency. We propose an efficient Video Object Proposal (VOP) generation method and show its efficacy in learning a better video object detector. A deep-learning based video object detector learned using the proposed VOP achieves state-of-the-art detection performance on the Youtube-Objects dataset. We further propose a clustering of VOPs which can efficiently be used for detecting objects in video in a streaming fashion. As opposed to applying per-frame convolutional neural network (CNN) based object detection, our proposed method called Objects in Video Enabler thRough LAbel Propagation (OVERLAP) needs to classify only a small fraction of all candidate proposals in every video frame through streaming clustering of object proposals and class-label propagation. Source code will be made available soon.

Connected through hyperedge HE-1 (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"
    - "person search in videos with one portrait through visual and temporal links"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "person search in videos with one portrait through visual and temporal links"):
  with members:
    - "person recognition in personal photo collections"
    - "unifying identification and context learning for person recognition"
    - "person search with natural language description"
    - "deep attributes driven multi camera person re identification"
    - "learning deep feature representations with domain guided dropout for person re identification"
    - "a comprehensive evaluation and benchmark for person re identification features metrics and datasets"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "joint detection and identification feature learning for person search"

Connected through hyperedge HE-3 (induced by source paper "context matters refining object detection in video with recurrent neural networks"):
  with members:
    - "object detection from video tubelets with convolutional neural networks"
    - "craft objects from images"
    - "inside outside net detecting objects in context with skip pooling and recurrent neural networks"
    - "analysing domain shift factors between videos and images for object detection"
    - "semi supervised sequence learning"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "overfeat integrated recognition localization and detection using 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)