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: "simultaneous object detection tracking and event recognition"
  Abstract: The common internal structure and algorithmic organization of object detection, detection-based tracking, and event recognition facilitates a general approach to integrating these three components. This supports multidirectional information flow between these components allowing object detection to influence tracking and event recognition and event recognition to influence tracking and object detection. The performance of the combination can exceed the performance of the components in isolation. This can be done with linear asymptotic complexity.

Connected through hyperedge HE-1 (induced by source paper "integrated object detection and tracking with tracklet conditioned detection"):
  with members:
    - "online multi object tracking with dual matching attention networks"
    - "cornernet detecting objects as paired keypoints"
    - "deformable convnets v2 more deformable better results"
    - "towards high performance video object detection"
    - "flow guided feature aggregation for video object detection"
    - "deformable convolutional networks"
    - "dual path networks"
    - "detect to track and track to detect"

Connected through hyperedge HE-2 (induced by source paper "deep sequential models for sampling based planning"):
  with members:
    - "inference compilation and universal probabilistic programming"
    - "deep sentence embedding using long short term memory networks analysis and application to information retrieval"
    - "batch informed trees bit sampling based optimal planning via the heuristically guided search of implicit random geometric graphs"
    - "monte carlo motion planning for robot trajectory optimization under uncertainty"
    - "detecting events and key actors in multi person videos"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "seeing what you re told sentence guided activity recognition in video"

Connected through hyperedge HE-3 (induced by source paper "combining deep learning and qualitative spatial reasoning to learn complex structures from sparse examples with noise"):
  with members:
    - "grounded language learning in a simulated 3d world"
    - "embodied question answering"
    - "unsupervised human action detection by action matching"
    - "voxml a visualization modeling language"
    - "unsupervised learning from narrated instruction videos"
    - "convolutional neural network architectures for matching natural language sentences"

Connected through hyperedge HE-4 (induced by source paper "collecting and annotating the large continuous action dataset"):
  with members:
    - "beyond short snippets deep networks for video classification"
    - "very deep convolutional networks for large scale image recognition"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "video in sentences out"
    - "imagenet large scale visual recognition challenge"
    - "a discriminative cnn video representation for event detection"
    - "seeing what you re told sentence guided activity recognition in video"
    - "ucf101 a dataset of 101 human actions classes from videos in the wild"

Connected through hyperedge HE-5 (induced by source paper "a faster method for tracking and scoring videos corresponding to sentences"):
  with members:
    - "seeing what you re told sentence guided activity recognition in video"

Connected through hyperedge HE-6 (induced by source paper "discriminative training learning to describe video with sentences from video described with sentences"):
  with members:
    - "video in sentences out"

Connected through hyperedge HE-7 (induced by source paper "seeing what you re told sentence guided activity recognition in video"):
  with members:
    - "video in sentences out"

Connected through hyperedge HE-8 (induced by source paper "saying what you re looking for linguistics meets video search"):
  with members:
    - "video in sentences out"

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)