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: "graph based global reasoning networks"
  Abstract: Globally modeling and reasoning over relations between regions can be beneficial for many computer vision tasks on both images and videos. Convolutional Neural Networks (CNNs) excel at modeling local relations by convolution operations, but they are typically inefficient at capturing global relations between distant regions and require stacking multiple convolution layers. In this work, we propose a new approach for reasoning globally in which a set of features are globally aggregated over the coordinate space and then projected to an interaction space where relational reasoning can be efficiently computed. After reasoning, relation-aware features are distributed back to the original coordinate space for down-stream tasks. We further present a highly efficient instantiation of the proposed approach and introduce the Global Reasoning unit (GloRe unit) that implements the coordinate-interaction space mapping by weighted global pooling and weighted broadcasting, and the relation reasoning via graph convolution on a small graph in interaction space. The proposed GloRe unit is lightweight, end-to-end trainable and can be easily plugged into existing CNNs for a wide range of tasks. Extensive experiments show our GloRe unit can consistently boost the performance of state-of-the-art backbone architectures, including ResNet, ResNeXt, SE-Net and DPN, for both 2D and 3D CNNs, on image classification, semantic segmentation and video action recognition task.

Connected through hyperedge HE-1 (induced by source paper "action graphs weakly supervised action localization with graph convolution networks"):
  with members:
    - "marginalized average attentional network for weakly supervised learning"
    - "dynamic graph modules for modeling higher order interactions in activity recognition"
    - "segregated temporal assembly recurrent networks for weakly supervised multiple action detection"
    - "w talc weakly supervised temporal activity localization and classification"
    - "neuralnetwork viterbi a framework for weakly supervised video learning"
    - "relational inductive biases deep learning and graph networks"
    - "dynamic graph modules for modeling object object interactions in activity recognition"
    - "a structured model for action detection"

Connected through hyperedge HE-2 (induced by source paper "dual graph convolutional network for semantic segmentation"):
  with members:
    - "global aggregation then local distribution in fully convolutional networks"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "ocnet object context network for scene parsing"
    - "learning a discriminative feature network for semantic segmentation"
    - "adaptive affinity fields for semantic segmentation"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "dual attention network for scene segmentation"

Connected through hyperedge HE-3 (induced by source paper "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"):
  with members:
    - "gluoncv and gluonnlp deep learning in computer vision and natural language processing"
    - "hetconv heterogeneous kernel based convolutions for deep cnns"
    - "deep high resolution representation learning for human pose estimation"
    - "elastic improving cnns with dynamic scaling policies"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "multi fiber networks for video recognition"
    - "regularized evolution for image classifier architecture search"
    - "a short note about kinetics 600"

Connected through hyperedge HE-4 (induced by source paper "adaptively connected neural networks"):
  with members:
    - "discovering underlying person structure pattern with relative local distance for person re identification"
    - "batch kalman normalization towards training deep neural networks with micro batches"
    - "spatial temporal person re identification"
    - "m2m gan many to many generative adversarial transfer learning for person re identification"
    - "occluded person re identification"
    - "large scale learnable graph convolutional networks"
    - "deformable convnets v2 more deformable better results"
    - "random erasing data augmentation"

Connected through hyperedge HE-5 (induced by source paper "context aware human motion prediction"):
  with members:
    - "how would surround vehicles move a unified framework for maneuver classification and motion prediction"
    - "bihmp gan bidirectional 3d human motion prediction gan"
    - "an lstm network for highway trajectory prediction"
    - "learning human object interactions by graph parsing neural networks"
    - "dynamic graph cnn for learning on point clouds"
    - "videos as space time region graphs"
    - "transferable interactiveness knowledge for human object interaction detection"
    - "structure inference net object detection using scene level context and instance level relationships"

Connected through hyperedge HE-6 (induced by source paper "deep reasoning with multi scale context for salient object detection"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "revisiting salient object detection simultaneous detection ranking and subitizing of multiple salient objects"
    - "videos as space time region graphs"
    - "reverse attention for salient object detection"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "learning uncertain convolutional features for accurate saliency detection"

Connected through hyperedge HE-7 (induced by source paper "graph reasoning networks for visual question answering"):
  with members:
    - "bottom up and top down attention for image captioning and visual question answering"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-8 (induced by source paper "dynamic graph modules for modeling object object interactions in activity recognition"):
  with members:
    - "eco efficient convolutional network for online video understanding"
    - "higher order network for action recognition"
    - "n ary relation extraction using graph state lstm"
    - "on the effectiveness of task granularity for transfer learning"
    - "relational inductive biases deep learning and graph networks"
    - "learning human object interactions by graph parsing neural networks"
    - "videos as space time region graphs"
    - "exploring graph structured passage representation for multi hop reading comprehension with graph 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)