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: "robust gait recognition by integrating inertial and rgbd sensors"
  Abstract: Gait has been considered as a promising and unique biometric for person identification. Traditionally, gait data are collected using either color sensors, such as a CCD camera, depth sensors, such as a Microsoft Kinect, or inertial sensors, such as an accelerometer. However, a single type of sensors may only capture part of the dynamic gait features and make the gait recognition sensitive to complex covariate conditions, leading to fragile gait-based person identification systems. In this paper, we propose to combine all three types of sensors for gait data collection and gait recognition, which can be used for important identification applications, such as identity recognition to access a restricted building or area. We propose two new algorithms, namely EigenGait and TrajGait, to extract gait features from the inertial data and the RGBD (color and depth) data, respectively. Specifically, EigenGait extracts general gait dynamics from the accelerometer readings in the eigenspace and TrajGait extracts more detailed sub-dynamics by analyzing 3D dense trajectories. Finally, both extracted features are fed into a supervised classifier for gait recognition and person identification. Experiments on 50 subjects, with comparisons to several other state-of-the-art gait-recognition approaches, show that the proposed approach can achieve higher recognition accuracy and robustness.

Connected through hyperedge HE-1 (induced by source paper "robust lane detection from continuous driving scenes using deep neural networks"):
  with members:
    - "a deep spatio temporal fuzzy neural network for passenger demand prediction"
    - "rethinking imagenet pre training"
    - "privacy preserving collaborative deep learning with unreliable participants"
    - "towards end to end lane detection an instance segmentation approach"
    - "improved deep hashing with soft pairwise similarity for multi label image retrieval"
    - "youtube vos sequence to sequence video object segmentation"
    - "el gan embedding loss driven generative adversarial networks for lane detection"
    - "decomposing motion and content for natural video sequence prediction"

Connected through hyperedge HE-2 (induced by source paper "on learning disentangled representations for gait recognition"):
  with members:
    - "representation learning by rotating your faces"
    - "on learning 3d face morphable model from in the wild images"
    - "towards high fidelity nonlinear 3d face morphable model"
    - "gait recognition via disentangled representation learning"
    - "retinaface single stage dense face localisation in the wild"
    - "synthesizing images of humans in unseen poses"
    - "towards highly accurate and stable face alignment for high resolution videos"
    - "disentangling features in 3d face shapes for joint face reconstruction and recognition"

Connected through hyperedge HE-3 (induced by source paper "improved deep hashing with soft pairwise similarity for multi label image retrieval"):
  with members:
    - "supervised discrete hashing with relaxation"
    - "deep supervised discrete hashing"
    - "hashnet deep learning to hash by continuation"
    - "deep supervised hashing with triplet labels"
    - "scalable image retrieval by sparse product quantization"
    - "deep cross modal hashing"
    - "instance aware hashing for multi label image retrieval"
    - "compact hash codes for efficient visual descriptors retrieval in large scale databases"

Connected through hyperedge HE-4 (induced by source paper "privacy preserving collaborative deep learning with irregular participants"):
  with members:
    - "xonn xnor based oblivious deep neural network inference"
    - "privynet a flexible framework for privacy preserving deep neural network training"
    - "chameleon a hybrid secure computation framework for machine learning applications"
    - "deep learning based gait recognition using smartphones in the wild"
    - "differentially private federated learning a client level perspective"
    - "deep models under the gan information leakage from collaborative deep learning"
    - "stealing machine learning models via prediction apis"
    - "membership inference attacks against machine learning models"

Connected through hyperedge HE-5 (induced by source paper "deep learning based gait recognition using smartphones in the wild"):
  with members:
    - "idnet smartphone based gait recognition with convolutional neural networks"
    - "deep recurrent convolutional networks for video based person re identification an end to end approach"
    - "deep convolutional and recurrent models for human activity recognition using wearables"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep activity recognition models with triaxial accelerometers"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "rrv a spatiotemporal descriptor for rigid body motion recognition"):
  with members:
    - "action recognition based on joint trajectory maps using convolutional 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)