human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "grounding tracking integration"):
  Abstract: In this paper, we study tracking by language that localizes the target box sequence in a video based on a language query. We propose a framework called GTI that decomposes the problem into three sub-tasks: Grounding, Tracking and Integration. The three sub-task modules operate simultaneously and predict the box sequence frame-by-frame. "Grounding" predicts the referred region directly from the language query. "Tracking" localizes the target based on the history of the grounded regions in previous frames. "Integration" generates final predictions by synergistically combining grounding and tracking. With the "integration" task as the key, we explore how to indicate the quality of the grounded regions in each frame and achieve the desired mutually beneficial combination. To this end, we propose an "RT-integration" method that defines and predicts two scores to guide the integration: 1) R-score represents the Region correctness whether the grounding prediction accurately covers the target, and 2) T-score represents the Template quality whether the region provides informative visual cues to improve tracking in future frames. We present our real-time GTI implementation with the proposed RT-integration, and benchmark the framework on LaSOT and Lingual OTB99 with highly promising results. Moreover, a disambiguated version of LaSOT queries can be used to facilitate future tracking by language studies.

Includes members:
  Member "learning discriminative model prediction for tracking":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "siamese box adaptive network for visual tracking")
  Member "zero shot grounding of objects from natural language queries":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi task collaborative network for joint referring expression comprehension and segmentation")
  Member "mask scoring r cnn":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "proposal learning for semi supervised object detection")
  Member "atom accurate tracking by overlap maximization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "siamese box adaptive network for visual tracking")
  Member "yolov3 an incremental improvement":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "real time referring expression comprehension by single stage grounding network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "variational context exploiting visual and textual context for grounding referring expressions")
  Member "mattnet modular attention network for referring expression comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "describe and attend to track learning natural language guided structural representation and visual attention for object tracking":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "physical virtual collaboration graph network for station level metro ridership prediction")

Question: Please tell me which class the source paper 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)