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: "me r cnn multi expert r cnn for object detection"
  Abstract: We introduce Multi-Expert Region-based CNN (ME R-CNN) which is equipped with multiple experts and built on top of the R-CNN framework known to be one of the state-of-the-art object detection methods. ME R-CNN focuses in better capturing the appearance variations caused by different shapes, poses, and viewing angles. The proposed approach consists of three experts each responsible for objects with particular shapes: horizontally elongated, square-like, and vertically elongated. On top of using selective search which provides a compact, yet effective set of region of interests (RoIs) for object detection, we augmented the set by also employing the exhaustive search for training only. Incorporating the exhaustive search can provide complementary advantages: i) it captures the multitude of neighboring RoIs missed by the selective search, and thus ii) provide significantly larger amount of training examples. We show that the ME R-CNN architecture provides considerable performance increase over the baselines on PASCAL VOC 07, 12, and MS COCO datasets.

Connected through hyperedge HE-1 (induced by source paper "centernet keypoint triplets for object detection"):
  with members:
    - "deep high resolution representation learning for human pose estimation"
    - "scale aware trident networks for object detection"
    - "objects as points"
    - "bottom up object detection by grouping extreme and center points"
    - "cornernet detecting objects as paired keypoints"
    - "grid r cnn"
    - "path aggregation network for instance segmentation"
    - "scratchdet training single shot object detectors from scratch"

Connected through hyperedge HE-2 (induced by source paper "delving into robust object detection from unmanned aerial vehicles a deep nuisance disentanglement approach"):
  with members:
    - "privacy preserving deep visual recognition an adversarial learning framework and a new dataset"
    - "enhance visual recognition under adverse conditions via deep networks"
    - "bridging the gap between computational photography and visual recognition"
    - "vision meets drones a challenge"
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "yolov3 an incremental improvement"
    - "modeling visual context is key to augmenting object detection datasets"
    - "domain adaptive faster r cnn for object detection in the wild"

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)