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: "structure inference machines recurrent neural networks for analyzing relations in group activity recognition"
  Abstract: Rich semantic relations are important in a variety of visual recognition problems. As a concrete example, group activity recognition involves the interactions and relative spatial relations of a set of people in a scene. State of the art recognition methods center on deep learning approaches for training highly effective, complex classifiers for interpreting images. However, bridging the relatively low-level concepts output by these methods to interpret higher-level compositional scenes remains a challenge. Graphical models are a standard tool for this task. In this paper, we propose a method to integrate graphical models and deep neural networks into a joint framework. Instead of using a traditional inference method, we use a sequential inference modeled by a recurrent neural network. Beyond this, the appropriate structure for inference can be learned by imposing gates on edges between nodes. Empirical results on group activity recognition demonstrate the potential of this model to handle highly structured learning tasks.

Connected through hyperedge HE-1 (induced by source paper "learning actor relation graphs for group activity recognition"):
  with members:
    - "linknet relational embedding for scene graph"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "graph r cnn for scene graph generation"
    - "actor centric relation network"
    - "learning human object interactions by graph parsing neural networks"
    - "videos as space time region graphs"
    - "factorizable net an efficient subgraph based framework for scene graph generation"
    - "non local neural networks"

Connected through hyperedge HE-2 (induced by source paper "neural message passing on hybrid spatio temporal visual and symbolic graphs for video understanding"):
  with members:
    - "dynamic graph modules for modeling higher order interactions in activity recognition"
    - "iterative visual reasoning beyond convolutions"
    - "structured label inference for visual understanding"
    - "actor centric relation network"
    - "video action transformer network"
    - "hybrid knowledge routed modules for large scale object detection"
    - "a structured model for action detection"
    - "learning human object interactions by graph parsing neural networks"

Connected through hyperedge HE-3 (induced by source paper "amortized bethe free energy minimization for learning mrfs"):
  with members:
    - "a tutorial on deep latent variable models of natural language"
    - "learning approximate inference networks for structured prediction"
    - "inference in probabilistic graphical models by graph neural networks"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "attention is all you need"
    - "neural variational inference and learning in undirected graphical models"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-4 (induced by source paper "deepsignals predicting intent of drivers through visual signals"):
  with members:
    - "end to end learning of multi sensor 3d tracking by detection"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "deep manta a coarse to fine many task network for joint 2d and 3d vehicle analysis from monocular image"
    - "mask r cnn"
    - "accurate single stage detector using recurrent rolling convolution"
    - "subcategory aware convolutional neural networks for object proposals and detection"
    - "deep watershed transform for instance segmentation"
    - "pyramid scene parsing network"

Connected through hyperedge HE-5 (induced by source paper "progressive relation learning for group activity recognition"):
  with members:
    - "an attention enhanced graph convolutional lstm network for skeleton based action recognition"
    - "learning actor relation graphs for group activity recognition"
    - "convolutional relational machine for group activity recognition"
    - "a comprehensive survey on graph neural networks"
    - "graph networks as learnable physics engines for inference and control"
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "structural recurrent neural network srnn for group activity analysis"
    - "skeleton based action recognition with synchronous local and non local spatio temporal learning and frequency attention"

Connected through hyperedge HE-6 (induced by source paper "putting visual object recognition in context"):
  with members:
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "lift the flap context reasoning using object centered graphs"
    - "iterative visual reasoning beyond convolutions"
    - "yolov3 an incremental improvement"
    - "modeling visual context is key to augmenting object detection datasets"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "learning what and where to attend"
    - "learning scene gist with convolutional neural networks to improve object recognition"

Connected through hyperedge HE-7 (induced by source paper "lift the flap what where and when for context reasoning"):
  with members:
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "finding any waldo with zero shot invariant and efficient visual search"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "learning scene gist with convolutional neural networks to improve object recognition"
    - "structure inference net object detection using scene level context and instance level relationships"
    - "scene graph generation by iterative message passing"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "graph structured representations for visual question answering"

Connected through hyperedge HE-8 (induced by source paper "you2me inferring body pose in egocentric video via first and second person interactions"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "simple baselines for human pose estimation and tracking"
    - "analyzing human human interactions a survey"
    - "detecting and recognizing human object interactions"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "learning feature pyramids for human pose estimation"
    - "detect and track efficient pose estimation in videos"

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)