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: "target driven instance detection"
  Abstract: While state-of-the-art general object detectors are getting better and better, there are not many systems specifically designed to take advantage of the instance detection problem. For many applications, such as household robotics, a system may need to recognize a few very specific instances at a time. Speed can be critical in these applications, as can the need to recognize previously unseen instances. We introduce a Target Driven Instance Detector(TDID), which modifies existing general object detectors for the instance recognition setting. TDID not only improves performance on instances seen during training, with a fast runtime, but is also able to generalize to detect novel instances.

Connected through hyperedge HE-1 (induced by source paper "template based unseen instance detection"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "deceptionnet network driven domain randomization"
    - "learning to track any object"
    - "on the convergence of adam and beyond"
    - "photorealistic image synthesis for object instance detection"
    - "target aware deep tracking"
    - "deep model based 6d pose refinement in rgb"
    - "bop benchmark for 6d object pose estimation"

Connected through hyperedge HE-2 (induced by source paper "an analysis of object representations in deep visual trackers"):
  with members:
    - "got 10k a large high diversity benchmark for generic object tracking in the wild"
    - "long term tracking in the wild a benchmark"
    - "siamrpn evolution of siamese visual tracking with very deep networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "towards accurate multi person pose estimation in the wild"
    - "detect to track and track to detect"
    - "learning to track at 100 fps with deep regression networks"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-3 (induced by source paper "learning to match templates for unseen instance detection"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "deceptionnet network driven domain randomization"
    - "learning to track any object"
    - "on the convergence of adam and beyond"
    - "photorealistic image synthesis for object instance detection"
    - "bop benchmark for 6d object pose estimation"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "fast online object tracking and segmentation a unifying approach"

Connected through hyperedge HE-4 (induced by source paper "combining deep learning and verification for precise object instance detection"):
  with members:
    - "selectivenet a deep neural network with an integrated reject option"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "dense object nets learning dense visual object descriptors by and for robotic manipulation"
    - "cut paste and learn surprisingly easy synthesis for instance detection"
    - "a dataset for developing and benchmarking active vision"
    - "synthesizing training data for object detection in indoor scenes"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "multiview rgb d dataset for object instance detection"

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)