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: "spatio temporal action detection with cascade proposal and location anticipation"
  Abstract: In this work, we address the problem of spatio-temporal action detection in temporally untrimmed videos. It is an important and challenging task as finding accurate human actions in both temporal and spatial space is important for analyzing large-scale video data. To tackle this problem, we propose a cascade proposal and location anticipation (CPLA) model for frame-level action detection. There are several salient points of our model: (1) a cascade region proposal network (casRPN) is adopted for action proposal generation and shows better localization accuracy compared with single region proposal network (RPN); (2) action spatio-temporal consistencies are exploited via a location anticipation network (LAN) and thus frame-level action detection is not conducted independently. Frame-level detections are then linked by solving an linking score maximization problem, and temporally trimmed into spatio-temporal action tubes. We demonstrate the effectiveness of our model on the challenging UCF101 and LIRIS-HARL datasets, both achieving state-of-the-art performance.

Connected through hyperedge HE-1 (induced by source paper "follow the attention combining partial pose and object motion for fine grained action detection"):
  with members:
    - "skeleton based action recognition of people handling objects"
    - "an empirical study of spatial attention mechanisms in deep networks"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "sample efficient deep reinforcement learning for dialogue systems with large action spaces"
    - "simple baselines for human pose estimation and tracking"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "rethinking the faster r cnn architecture for temporal action localization"

Connected through hyperedge HE-2 (induced by source paper "dance with flow two in one stream action detection"):
  with members:
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "videocapsulenet a simplified network for action detection"
    - "a better baseline for ava"
    - "actor centric relation network"
    - "modulating early visual processing by language"
    - "amtnet action micro tube regression by end to end trainable deep architecture"
    - "rethinking spatiotemporal feature learning speed accuracy trade offs in video classification"
    - "arbitrary style transfer in real time with adaptive instance normalization"

Connected through hyperedge HE-3 (induced by source paper "pointly supervised action localization"):
  with members:
    - "amtnet action micro tube regression by end to end trainable deep architecture"
    - "extreme clicking for efficient object annotation"
    - "localizing actions from video labels and pseudo annotations"
    - "action tubelet detector for spatio temporal action localization"
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "spatial aware object embeddings for zero shot localization and classification of actions"
    - "pathtrack fast trajectory annotation with path supervision"
    - "tube convolutional neural network t cnn for action detection in videos"

Connected through hyperedge HE-4 (induced by source paper "remote photoplethysmograph signal measurement from facial videos using spatio temporal networks"):
  with members:
    - "deepphys video based physiological measurement using convolutional attention networks"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "rethinking spatiotemporal feature learning speed accuracy trade offs in video classification"
    - "temporal convolutional networks for action segmentation and detection"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "learning spatiotemporal features with 3d convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "every pixel counts joint learning of geometry and motion with 3d holistic understanding"):
  with members:
    - "adversarial collaboration joint unsupervised learning of depth camera motion optical flow and motion segmentation"
    - "every pixel counts unsupervised geometry learning with holistic 3d motion understanding"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "lego learning edge with geometry all at once by watching videos"
    - "digging into self supervised monocular depth estimation"

Connected through hyperedge HE-6 (induced by source paper "online detection of action start in untrimmed streaming videos"):
  with members:
    - "multi fiber networks for video recognition"
    - "ctap complementary temporal action proposal generation"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "good semi supervised learning that requires a bad gan"
    - "single shot temporal action detection"
    - "predictive corrective networks for action detection"
    - "temporal action detection with structured segment networks"
    - "cascaded boundary regression for temporal action detection"

Connected through hyperedge HE-7 (induced by source paper "every pixel counts unsupervised geometry learning with holistic 3d motion understanding"):
  with members:
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "lego learning edge with geometry all at once by watching videos"
    - "monocular dense 3d reconstruction of a complex dynamic scene from two perspective frames"
    - "learning to segment moving objects"
    - "pixel level matching for video object segmentation using convolutional neural networks"
    - "unsupervised learning of depth and ego motion from video"
    - "learning depth from monocular videos using direct methods"

Connected through hyperedge HE-8 (induced by source paper "msnet mutual suppression network for disentangled video representations"):
  with members:
    - "decomposing motion and content for natural video sequence prediction"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "folded recurrent neural networks for future video prediction"
    - "unsupervised learning of disentangled representations from video"
    - "deep learning for precipitation nowcasting a benchmark and a new model"
    - "h denseunet hybrid densely connected unet for liver and liver tumor segmentation from ct volumes"
    - "an end to end 3d convolutional neural network for action detection and segmentation in videos"
    - "mocogan decomposing motion and content for video generation"

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)