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: "scene flow to action map a new representation for rgb d based action recognition with convolutional neural networks"
  Abstract: Scene flow describes the motion of 3D objects in real world and potentially could be the basis of a good feature for 3D action recognition. However, its use for action recognition, especially in the context of convolutional neural networks (ConvNets), has not been previously studied. In this paper, we propose the extraction and use of scene flow for action recognition from RGB-D data. Previous works have considered the depth and RGB modalities as separate channels and extract features for later fusion. We take a different approach and consider the modalities as one entity, thus allowing feature extraction for action recognition at the beginning. Two key questions about the use of scene flow for action recognition are addressed: how to organize the scene flow vectors and how to represent the long term dynamics of videos based on scene flow. In order to calculate the scene flow correctly on the available datasets, we propose an effective self-calibration method to align the RGB and depth data spatially without knowledge of the camera parameters. Based on the scene flow vectors, we propose a new representation, namely, Scene Flow to Action Map (SFAM), that describes several long term spatio-temporal dynamics for action recognition. We adopt a channel transform kernel to transform the scene flow vectors to an optimal color space analogous to RGB. This transformation takes better advantage of the trained ConvNets models over ImageNet. Experimental results indicate that this new representation can surpass the performance of state-of-the-art methods on two large public datasets.

Connected through hyperedge HE-1 (induced by source paper "correlation net spatiotemporal multimodal deep learning for action recognition"):
  with members:
    - "outer product based neural collaborative filtering"
    - "neural collaborative filtering"
    - "ts lstm and temporal inception exploiting spatiotemporal dynamics for activity recognition"
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "temporal segment networks towards good practices for deep action recognition"
    - "convolutional two stream network fusion for video action recognition"
    - "long term temporal convolutions for action recognition"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-2 (induced by source paper "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"):
  with members:
    - "feature boosting network for 3d pose estimation"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "glimpse clouds human activity recognition from unstructured feature points"
    - "video representation learning using discriminative pooling"
    - "learning and refining of privileged information based rnns for action recognition from depth sequences"
    - "sphereface deep hypersphere embedding for face recognition"
    - "when kernel methods meet feature learning log covariance network for action recognition from skeletal data"
    - "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "action recognition using volumetric motion representations"):
  with members:
    - "glimpse clouds human activity recognition from unstructured feature points"
    - "unsupervised learning of view invariant action representations"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "two stream rnn cnn for action recognition in 3d videos"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "cooperative training of deep aggregation networks for rgb d action recognition"
    - "unsupervised learning of long term motion dynamics for videos"
    - "chained multi stream networks exploiting pose motion and appearance for action classification and detection"

Connected through hyperedge HE-4 (induced by source paper "translate to recognize networks for rgb d scene recognition"):
  with members:
    - "learning effective rgb d representations for scene recognition"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "knowledge guided disambiguation for large scale scene classification with multi resolution cnns"
    - "perceptual losses for real time style transfer and super resolution"
    - "generative image modeling using style and structure adversarial networks"
    - "deeper depth prediction with fully convolutional residual networks"
    - "image to image translation with conditional adversarial networks"
    - "colorful image colorization"

Connected through hyperedge HE-5 (induced by source paper "chalearn looking at people isogd and congd large scale rgb d gesture recognition"):
  with members:
    - "bi directional block self attention for fast and memory efficient sequence modeling"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "cooperative training of deep aggregation networks for rgb d action recognition"
    - "rank pooling for action recognition"
    - "hand keypoint detection in single images using multiview bootstrapping"
    - "automatic recognition of facial displays of unfelt emotions"
    - "convolutional pose machines"
    - "large scale continuous gesture recognition using convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "sa net deep neural network for robot trajectory recognition from rgb d streams"):
  with members:
    - "merging in congested freeway traffic using multipolicy decision making and passive actor critic learning"
    - "yolov3 an incremental improvement"
    - "temporal activity detection in untrimmed videos with recurrent neural networks"
    - "beyond short snippets deep networks for video classification"
    - "deep residual learning for image recognition"
    - "maximum entropy deep inverse reinforcement learning"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "multimodal deep learning for robust rgb d object recognition"

Connected through hyperedge HE-7 (induced by source paper "unsupervised learning of view invariant action representations"):
  with members:
    - "unsupervised representation learning by predicting image rotations"
    - "transitive invariance for self supervised visual representation learning"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "unsupervised representation learning by sorting sequences"
    - "representation learning by learning to count"
    - "unsupervised learning of long term motion dynamics for videos"
    - "view adaptive recurrent neural networks for high performance human action recognition from skeleton data"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"

Connected through hyperedge HE-8 (induced by source paper "learning rigidity in dynamic scenes with a moving camera for 3d motion field estimation"):
  with members:
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "pyramid stereo matching network"
    - "optical flow in mostly rigid scenes"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "unsupervised learning of depth and ego motion from video"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "learning from synthetic humans"
    - "demon depth and motion network for learning monocular stereo"

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)