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: "two stream flow guided convolutional attention networks for action recognition"
  Abstract: This paper proposes a two-stream flow-guided convolutional attention networks for action recognition in videos. The central idea is that optical flows, when properly compensated for the camera motion, can be used to guide attention to the human foreground. We thus develop cross-link layers from the temporal network (trained on flows) to the spatial network (trained on RGB frames). These cross-link layers guide the spatial-stream to pay more attention to the human foreground areas and be less affected by background clutter. We obtain promising performances with our approach on the UCF101, HMDB51 and Hollywood2 datasets.

Connected through hyperedge HE-1 (induced by source paper "salient object detection with lossless feature reflection and weighted structural loss"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "agile amulet real time salient object detection with contextual attention"
    - "boundary guided feature aggregation network for salient object detection"
    - "interactive deep colorization with simultaneous global and local inputs"
    - "learning uncertain convolutional features for accurate saliency detection"
    - "structure measure a new way to evaluate foreground maps"
    - "picanet learning pixel wise contextual attention for saliency detection"
    - "amulet aggregating multi level convolutional features for salient object detection"

Connected through hyperedge HE-2 (induced by source paper "analysis of cnn based remote ppg to understand limitations and sensitivities"):
  with members:
    - "efficient real time camera based estimation of heart rate and its variability"
    - "remote photoplethysmograph signal measurement from facial videos using spatio temporal networks"
    - "deepphys video based physiological measurement using convolutional attention networks"
    - "imagenet large scale visual recognition challenge"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-3 (induced by source paper "multi fiber networks for video recognition"):
  with members:
    - "online detection of action start in untrimmed streaming videos"
    - "maskconnect connectivity learning by gradient descent"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "compressed video action recognition"
    - "appearance and relation networks for video classification"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"

Connected through hyperedge HE-4 (induced by source paper "deepphys video based physiological measurement using convolutional attention networks"):
  with members:
    - "estimating carotid pulse and breathing rate from near infrared video of the neck"
    - "unsupervised learning for physical interaction through video prediction"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "visual dynamics probabilistic future frame synthesis via cross convolutional networks"
    - "videolstm convolves attends and flows for action recognition"
    - "delving deeper into convolutional networks for learning video representations"
    - "flownet learning optical flow with convolutional networks"
    - "beyond short snippets deep networks for video classification"

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)