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: "predictive corrective networks for action detection"
  Abstract: While deep feature learning has revolutionized techniques for static-image understanding, the same does not quite hold for video processing. Architectures and optimization techniques used for video are largely based off those for static images, potentially underutilizing rich video information. In this work, we rethink both the underlying network architecture and the stochastic learning paradigm for temporal data. To do so, we draw inspiration from classic theory on linear dynamic systems for modeling time series. By extending such models to include nonlinear mappings, we derive a series of novel recurrent neural networks that sequentially make top-down predictions about the future and then correct those predictions with bottom-up observations. Predictive-corrective networks have a number of desirable properties: (1) they can adaptively focus computation on surprising frames where predictions require large corrections, (2) they simplify learning in that only residual-like corrective terms need to be learned over time and (3) they naturally decorrelate an input data stream in a hierarchical fashion, producing a more reliable signal for learning at each layer of a network. We provide an extensive analysis of our lightweight and interpretable framework, and demonstrate that our model is competitive with the two-stream network on three challenging datasets without the need for computationally expensive optical flow.

Connected through hyperedge HE-1 (induced by source paper "grab fast and accurate sensor processing for cashier free shopping"):
  with members:
    - "features for multi target multi camera tracking and re identification"
    - "yolov3 an incremental improvement"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "scalable person re identification on supervised smoothed manifold"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "action tubelet detector for spatio temporal action localization"
    - "simple online and realtime tracking with a deep association metric"
    - "scale aware face detection"

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 "progress regression rnn for online spatial temporal action localization in unconstrained videos"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "amtnet action micro tube regression by end to end trainable deep architecture"
    - "deep learning for precipitation nowcasting a benchmark and a new model"
    - "action tubelet detector for spatio temporal action localization"
    - "temporal action localization by structured maximal sums"
    - "chained multi stream networks exploiting pose motion and appearance for action classification and detection"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"

Connected through hyperedge HE-4 (induced by source paper "learning differentiable grammars for continuous data"):
  with members:
    - "fine grained activity recognition in baseball videos"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "r c3d region convolutional 3d network for temporal activity detection"
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "recurrent neural network grammars"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "asynchronous temporal fields for action recognition"
    - "every moment counts dense detailed labeling of actions in complex videos"

Connected through hyperedge HE-5 (induced by source paper "early detection of injuries in mlb pitchers from video"):
  with members:
    - "fine grained activity recognition in baseball videos"
    - "evolving space time neural architectures for videos"
    - "representation flow for action recognition"
    - "slowfast networks for video recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "on the integration of optical flow and action recognition"
    - "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos"
    - "the kinetics human action video dataset"

Connected through hyperedge HE-6 (induced by source paper "recurrent residual module for fast inference in videos"):
  with members:
    - "moments in time dataset one million videos for event understanding"
    - "performance guaranteed network acceleration via high order residual quantization"
    - "what actions are needed for understanding human actions in videos"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "thinet a filter level pruning method for deep neural network compression"
    - "the kinetics human action video dataset"
    - "the power of sparsity in convolutional neural networks"
    - "speeding up convolutional neural networks by exploiting the sparsity of rectifier units"

Connected through hyperedge HE-7 (induced by source paper "autoloc weakly supervised temporal action localization"):
  with members:
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "single shot temporal action detection"
    - "weakly supervised semantic segmentation using web crawled videos"
    - "temporal action detection with structured segment networks"
    - "deep self taught learning for weakly supervised object localization"
    - "two phase learning for weakly supervised object localization"
    - "untrimmednets for weakly supervised action recognition and detection"

Connected through hyperedge HE-8 (induced by source paper "temporal recurrent networks for online action detection"):
  with members:
    - "unsupervised traffic accident detection in first person videos"
    - "real world anomaly detection in surveillance videos"
    - "fully coupled two stream spatiotemporal networks for extremely low resolution action recognition"
    - "online action detection in untrimmed streaming videos modeling and evaluation"
    - "toward driving scene understanding a dataset for learning driver behavior and causal reasoning"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "non local 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)