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: "caffe convolutional architecture for fast feature embedding"
  Abstract: Caffe provides multimedia scientists and practitioners with a clean and modifiable framework for state-of-the-art deep learning algorithms and a collection of reference models. The framework is a BSD-licensed C++ library with Python and MATLAB bindings for training and deploying general-purpose convolutional neural networks and other deep models efficiently on commodity architectures. Caffe fits industry and internet-scale media needs by CUDA GPU computation, processing over 40 million images a day on a single K40 or Titan GPU ($\approx$ 2.5 ms per image). By separating model representation from actual implementation, Caffe allows experimentation and seamless switching among platforms for ease of development and deployment from prototyping machines to cloud environments. Caffe is maintained and developed by the Berkeley Vision and Learning Center (BVLC) with the help of an active community of contributors on GitHub. It powers ongoing research projects, large-scale industrial applications, and startup prototypes in vision, speech, and multimedia.

Connected through hyperedge HE-1 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark"):
  with members:
    - "cornernet detecting objects as paired keypoints"
    - "gabor convolutional networks"
    - "recent advances in object detection in the age of deep convolutional neural networks"
    - "deep learning for generic object detection a survey"
    - "yolov3 an incremental improvement"
    - "path aggregation network for instance segmentation"
    - "sniper efficient multi scale training"
    - "deep adaptive proposal network for object detection in optical remote sensing images"

Connected through hyperedge HE-2 (induced by source paper "hyper parameter optimization a review of algorithms and applications"):
  with members:
    - "a generalized framework for population based training"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "meta learning a survey"
    - "deep learning using rectified linear units relu"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "massively parallel hyperparameter tuning"
    - "combination of hyperband and bayesian optimization for hyperparameter optimization in deep learning"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-3 (induced by source paper "grad cam visual explanations from deep networks via gradient based localization"):
  with members:
    - "taking a hint leveraging explanations to make vision and language models more grounded"
    - "choose your neuron incorporating domain knowledge through neuron importance"
    - "network dissection quantifying interpretability of deep visual representations"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "iqa visual question answering in interactive environments"
    - "embodied question answering"
    - "analyzing the behavior of visual question answering models"

Connected through hyperedge HE-4 (induced by source paper "the deep learning compiler a comprehensive survey"):
  with members:
    - "shredder learning noise distributions to protect inference privacy"
    - "fusionstitching boosting execution efficiency of memory intensive computations for dl workloads"
    - "stripe tensor compilation via the nested polyhedral model"
    - "tensorflow eager a multi stage python embedded dsl for machine learning"
    - "privacy for rescue a new testimony why privacy is vulnerable in deep models"
    - "dissecting the graphcore ipu architecture via microbenchmarking"
    - "benchmarking tpu gpu and cpu platforms for deep learning"
    - "a comprehensive survey on graph neural networks"

Connected through hyperedge HE-5 (induced by source paper "deep multicameral decoding for localizing unoccluded object instances from a single rgb image"):
  with members:
    - "fully dense unet for 2 d sparse photoacoustic tomography artifact removal"
    - "dual graph convolutional network for semantic segmentation"
    - "pointrend image segmentation as rendering"
    - "quantized densely connected u nets for efficient landmark localization"
    - "object segmentation in depth maps with one user click and a synthetically trained fully convolutional network"
    - "mvtec d2s densely segmented supermarket dataset"
    - "deep ordinal regression network for monocular depth estimation"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"

Connected through hyperedge HE-6 (induced by source paper "har net joint learning of hybrid attention for single stage object detection"):
  with members:
    - "deep ordinal hashing with spatial attention"
    - "feature selective anchor free module for single shot object detection"
    - "sharp attention network via adaptive sampling for person re identification"
    - "group normalization"
    - "yolov3 an incremental improvement"
    - "gradient harmonized single stage detector"
    - "soft nms improving object detection with one line of code"
    - "focal loss for dense object detection"

Connected through hyperedge HE-7 (induced by source paper "semantic image networks for human action recognition"):
  with members:
    - "spatiotemporal pyramid network for video action recognition"
    - "multi fiber networks for video recognition"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "spatio temporal channel correlation networks for action classification"
    - "a 2 nets double attention networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "appearance and relation networks for video classification"

Connected through hyperedge HE-8 (induced by source paper "cocopie making mobile ai sweet as pie compression compilation co design goes a long way"):
  with members:
    - "patdnn achieving real time dnn execution on mobile devices with pattern based weight pruning"
    - "pconv the missing but desirable sparsity in dnn weight pruning for real time execution on mobile devices"
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "wide activation for efficient and accurate image super resolution"
    - "multi style generative network for real time transfer"
    - "extremely low bit neural network squeeze the last bit out with admm"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "thinet a filter level pruning method for deep neural network compression"

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)