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: "detecting events and key actors in multi person videos"
  Abstract: Multi-person event recognition is a challenging task, often with many people active in the scene but only a small subset contributing to an actual event. In this paper, we propose a model which learns to detect events in such videos while automatically "attending" to the people responsible for the event. Our model does not use explicit annotations regarding who or where those people are during training and testing. In particular, we track people in videos and use a recurrent neural network (RNN) to represent the track features. We learn time-varying attention weights to combine these features at each time-instant. The attended features are then processed using another RNN for event detection/classification. Since most video datasets with multiple people are restricted to a small number of videos, we also collected a new basketball dataset comprising 257 basketball games with 14K event annotations corresponding to 11 event classes. Our model outperforms state-of-the-art methods for both event classification and detection on this new dataset. Additionally, we show that the attention mechanism is able to consistently localize the relevant players.

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 "reasoning about human object interactions through dual attention networks"):
  with members:
    - "on the effectiveness of task granularity for transfer learning"
    - "learning human object interactions by graph parsing neural networks"
    - "videos as space time region graphs"
    - "tsm temporal shift module for efficient video understanding"
    - "detecting and recognizing human object interactions"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "non local neural networks"
    - "squeeze and excitation networks"

Connected through hyperedge HE-3 (induced by source paper "spin a high speed high resolution vision dataset for tracking and action recognition in ping pong"):
  with members:
    - "stochastic prediction of multi agent interactions from partial observations"
    - "dynamonet dynamic action and motion network"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "tracking noisy targets a review of recent object tracking approaches"
    - "a proposal based solution to spatio temporal action detection in untrimmed videos"
    - "video action transformer network"
    - "representation flow for action recognition"
    - "a 2 nets double attention networks"

Connected through hyperedge HE-4 (induced by source paper "a context aware loss function for action spotting in soccer videos"):
  with members:
    - "golfdb a video database for golf swing sequencing"
    - "gaussian temporal awareness networks for action localization"
    - "pan tilt zoom slam for sports videos"
    - "bmn boundary matching network for temporal action proposal generation"
    - "fine grained activity recognition in baseball videos"
    - "diagnosing error in temporal action detectors"
    - "temporal hockey action recognition via pose and optical flows"
    - "taskonomy disentangling task transfer learning"

Connected through hyperedge HE-5 (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-6 (induced by source paper "spatiotemporal co attention recurrent neural networks for human skeleton motion prediction"):
  with members:
    - "hierarchical long short term concurrent memory for human interaction recognition"
    - "long term human motion prediction by modeling motion context and enhancing motion dynamic"
    - "learning and refining of privileged information based rnns for action recognition from depth sequences"
    - "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks"
    - "deep representation learning for human motion prediction and classification"
    - "skeleton based action recognition using spatio temporal lstm network with trust gates"
    - "on human motion prediction using recurrent neural networks"
    - "view adaptive recurrent neural networks for high performance human action recognition from skeleton data"

Connected through hyperedge HE-7 (induced by source paper "convolutional relational machine for group activity recognition"):
  with members:
    - "towards vision based smart hospitals a system for tracking and monitoring hand hygiene compliance"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "unsupervised learning of long term motion dynamics for videos"
    - "cern confidence energy recurrent network for group activity recognition"
    - "convolutional pose machines"
    - "social scene understanding end to end multi person action localization and collective activity recognition"
    - "temporal segment networks towards good practices for deep action recognition"
    - "convolutional two stream network fusion for video action recognition"

Connected through hyperedge HE-8 (induced by source paper "multi granularity reasoning for social relation recognition from images"):
  with members:
    - "deep reasoning with knowledge graph for social relationship understanding"
    - "simple baselines for human pose estimation and tracking"
    - "exact and consistent interpretation for piecewise linear neural networks a closed form solution"
    - "a domain based approach to social relation recognition"
    - "dual glance model for deciphering social relationships"
    - "mask r cnn"
    - "semantic object parsing with graph lstm"
    - "from facial expression recognition to interpersonal relation prediction"

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)