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 "robust visual object tracking with natural language region proposal network"):
  Abstract: Tracking with natural-language (NL) specification is a powerful new paradigm to yield trackers that initialize without a manually-specified bounding box, stay on target in spite of occlusions, and auto-recover when diverged. These advantages stem in part from visual appearance and NL having distinct and complementary invariance properties. However, realizing these advantages is technically challenging: the two modalities have incompatible representations. In this paper, we present the first practical and competitive solution to the challenge of tracking with NL specification. Our first novelty is an NL region proposal network (NL-RPN) that transforms an NL description into a convolutional kernel and shares the search branch with siamese trackers; the combined network can be trained end-to-end. Secondly, we propose a novel formulation to represent the history of past visual exemplars and use those exemplars to automatically reset the tracker together with our NL-RPN. Empirical results over tracking benchmarks with NL annotations demonstrate the effectiveness of our approach.

Includes members:
  Member "tell me what to track":
    also connected through other hyperedges:
      (none)
  Member "language features matter effective language representations for vision language tasks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "component analysis for visual question answering architectures")
  Member "a fast and accurate one stage approach to visual grounding":
    also connected through other hyperedges:
      (none)
  Member "universal sentence encoder":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "contextual lensing of universal sentence representations")
  Member "siamrpn evolution of siamese visual tracking with very deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "siamese box adaptive network for visual tracking")
  Member "vital visual tracking via adversarial learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "siamese box adaptive network for visual tracking")
  Member "end to end representation learning for correlation filter based tracking":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "siamese attentional keypoint network for high performance visual tracking")
  Member "learning two branch neural networks for image text matching tasks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "latent normalizing flows for many to many cross domain mappings")

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)