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: "deepphys video based physiological measurement using convolutional attention networks"
  Abstract: Non-contact video-based physiological measurement has many applications in health care and human-computer interaction. Practical applications require measurements to be accurate even in the presence of large head rotations. We propose the first end-to-end system for video-based measurement of heart and breathing rate using a deep convolutional network. The system features a new motion representation based on a skin reflection model and a new attention mechanism using appearance information to guide motion estimation, both of which enable robust measurement under heterogeneous lighting and major motions. Our approach significantly outperforms all current state-of-the-art methods on both RGB and infrared video datasets. Furthermore, it allows spatial-temporal distributions of physiological signals to be visualized via the attention mechanism.

Connected through hyperedge HE-1 (induced by source paper "fakecatcher detection of synthetic portrait videos using biological signals"):
  with members:
    - "video to video synthesis"
    - "mesonet a compact facial video forgery detection network"
    - "faceforensics a large scale video dataset for forgery detection in human faces"
    - "ganimation anatomically aware facial animation from a single image"
    - "detection of deep network generated images using disparities in color components"
    - "in ictu oculi exposing ai generated fake face videos by detecting eye blinking"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-2 (induced by source paper "remote photoplethysmograph signal measurement from facial videos using spatio temporal networks"):
  with members:
    - "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"
    - "spatio temporal action detection with cascade proposal and location anticipation"
    - "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-3 (induced by source paper "rhythmnet end to end heart rate estimation from face via spatial temporal representation"):
  with members:
    - "remote photoplethysmograph signal measurement from facial videos using spatio temporal networks"
    - "vipl hr a multi modal database for pulse estimation from less constrained face video"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-4 (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"
    - "two stream flow guided convolutional attention networks for action recognition"
    - "imagenet large scale visual recognition challenge"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-5 (induced by source paper "architectural tricks for deep learning in remote photoplethysmography"):
  with members:
    - "recovering remote photoplethysmograph signal from facial videos using spatio temporal convolutional networks"
    - "super convergence very fast training of residual networks using large learning rates"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "no more pesky learning rate guessing games"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "iphys an open non contact imaging based physiological measurement toolbox"):
  with members:
    - "estimating carotid pulse and breathing rate from near infrared video of the neck"

Connected through hyperedge HE-7 (induced by source paper "efficient real time camera based estimation of heart rate and its variability"):
  with members:
    - "video acceleration magnification"

Connected through hyperedge HE-8 (induced by source paper "recovering remote photoplethysmograph signal from facial videos using spatio temporal convolutional networks"):
  with members:
    - "learning spatiotemporal features with 3d convolutional 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)