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: "adaptive feeding achieving fast and accurate detections by adaptively combining object detectors"
  Abstract: Object detection aims at high speed and accuracy simultaneously. However, fast models are usually less accurate, while accurate models cannot satisfy our need for speed. A fast model can be 10 times faster but 50\% less accurate than an accurate model. In this paper, we propose Adaptive Feeding (AF) to combine a fast (but less accurate) detector and an accurate (but slow) detector, by adaptively determining whether an image is easy or hard and choosing an appropriate detector for it. In practice, we build a cascade of detectors, including the AF classifier which make the easy vs. hard decision and the two detectors. The AF classifier can be tuned to obtain different tradeoff between speed and accuracy, which has negligible training time and requires no additional training data. Experimental results on the PASCAL VOC, MS COCO and Caltech Pedestrian datasets confirm that AF has the ability to achieve comparable speed as the fast detector and comparable accuracy as the accurate one at the same time. As an example, by combining the fast SSD300 with the accurate SSD500 detector, AF leads to 50\% speedup over SSD500 with the same precision on the VOC2007 test set.

Connected through hyperedge HE-1 (induced by source paper "a novel design of adaptive and hierarchical convolutional neural networks using partial reconfiguration on fpga"):
  with members:
    - "convolutional neural networks ensemble modeling fine tuning and unsupervised semantic localization for neurosurgical cle images"
    - "skipnet learning dynamic routing in convolutional networks"
    - "adaptive neural networks for efficient inference"
    - "branchynet fast inference via early exiting from deep neural networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "finn a framework for fast scalable binarized neural network inference"
    - "learning structured sparsity in deep neural networks"
    - "yolo9000 better faster stronger"

Connected through hyperedge HE-2 (induced by source paper "elastic improving cnns with dynamic scaling policies"):
  with members:
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "deep layer aggregation"
    - "dilated residual networks"
    - "squeeze and excitation networks"
    - "condensenet an efficient densenet using learned group convolutions"
    - "learning spatial regularization with image level supervisions for multi label image classification"
    - "learning a wavelet like auto encoder to accelerate deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "screenernet learning self paced curriculum for deep neural networks"):
  with members:
    - "growing a brain fine tuning by increasing model capacity"
    - "automated curriculum learning for neural networks"
    - "hindsight experience replay"
    - "focal loss for dense object detection"
    - "intrinsic motivation and automatic curricula via asymmetric self play"
    - "prioritized experience replay"
    - "online batch selection for faster training of neural networks"
    - "scheduled sampling for sequence prediction with recurrent neural networks"

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)