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: "learning region features for object detection"
  Abstract: While most steps in the modern object detection methods are learnable, the region feature extraction step remains largely hand-crafted, featured by RoI pooling methods. This work proposes a general viewpoint that unifies existing region feature extraction methods and a novel method that is end-to-end learnable. The proposed method removes most heuristic choices and outperforms its RoI pooling counterparts. It moves further towards fully learnable object detection.

Connected through hyperedge HE-1 (induced by source paper "deep relational reasoning graph network for arbitrary shape text detection"):
  with members:
    - "arbitrary shape scene text detection with adaptive text region representation"
    - "look more than once an accurate detector for text of arbitrary shapes"
    - "textfield learning a deep direction field for irregular scene text detection"
    - "character region awareness for text detection"
    - "linkage based face clustering via graph convolution network"
    - "msr multi scale shape regression for scene text detection"
    - "efficient and accurate arbitrary shaped text detection with pixel aggregation network"
    - "towards robust curve text detection with conditional spatial expansion"

Connected through hyperedge HE-2 (induced by source paper "bridging the gap between anchor based and anchor free detection via adaptive training sample selection"):
  with members:
    - "reppoints point set representation for object detection"
    - "foveabox beyond anchor based object detector"
    - "libra r cnn towards balanced learning for object detection"
    - "centernet keypoint triplets for object detection"
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "region proposal by guided anchoring"
    - "high level semantic feature detection a new perspective for pedestrian detection"
    - "towards accurate one stage object detection with ap loss"

Connected through hyperedge HE-3 (induced by source paper "an empirical study of spatial attention mechanisms in deep networks"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "attention is not explanation"
    - "transformer xl attentive language models beyond a fixed length context"
    - "ocnet object context network for scene parsing"
    - "self attention with relative position representations"
    - "ccnet criss cross attention for semantic segmentation"
    - "interpreting recurrent and attention based neural models a case study on natural language inference"
    - "deformable convnets v2 more deformable better results"

Connected through hyperedge HE-4 (induced by source paper "graph representation learning for audio music genre classification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "bottom up broadcast neural network for music genre classification"
    - "fast graph representation learning with pytorch geometric"
    - "exploiting semantics in neural machine translation with graph convolutional networks"
    - "graph to sequence learning using gated graph neural networks"
    - "graph convolutional networks for text classification"
    - "deepinf social influence prediction with deep learning"
    - "relational inductive biases deep learning and graph networks"

Connected through hyperedge HE-5 (induced by source paper "graph optimized convolutional networks"):
  with members:
    - "learning human object interactions by graph parsing neural networks"
    - "rgcnn regularized graph cnn for point cloud segmentation"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "deep convolutional networks on graph structured data"
    - "convolutional networks on graphs for learning molecular fingerprints"

Connected through hyperedge HE-6 (induced by source paper "deep regionlets blended representation and deep learning for generic object detection"):
  with members:
    - "an analysis of pre training on object detection"
    - "delving into robust object detection from unmanned aerial vehicles a deep nuisance disentanglement approach"
    - "cornernet detecting objects as paired keypoints"
    - "crystal loss and quality pooling for unconstrained face verification and recognition"
    - "a fast and accurate system for face detection identification and verification"
    - "deep feature pyramid reconfiguration for object detection"
    - "multi scale location aware kernel representation for object detection"
    - "pseudo mask augmented object detection"

Connected through hyperedge HE-7 (induced by source paper "deformable convnets v2 more deformable better results"):
  with members:
    - "revisiting rcnn on awakening the classification power of faster rcnn"
    - "non local neural networks"
    - "visual interaction networks"
    - "flow guided feature aggregation for video object detection"
    - "attention is all you need"
    - "relation networks for object detection"
    - "a simple neural network module for relational reasoning"
    - "active convolution learning the shape of convolution for image classification"

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)