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: "learning human identity from motion patterns"
  Abstract: We present a large-scale study exploring the capability of temporal deep neural networks to interpret natural human kinematics and introduce the first method for active biometric authentication with mobile inertial sensors. At Google, we have created a first-of-its-kind dataset of human movements, passively collected by 1500 volunteers using their smartphones daily over several months. We (1) compare several neural architectures for efficient learning of temporal multi-modal data representations, (2) propose an optimized shift-invariant dense convolutional mechanism (DCWRNN), and (3) incorporate the discriminatively-trained dynamic features in a probabilistic generative framework taking into account temporal characteristics. Our results demonstrate that human kinematics convey important information about user identity and can serve as a valuable component of multi-modal authentication systems. Finally, we demonstrate that the proposed model can be successfully applied also in a visual context.

Connected through hyperedge HE-1 (induced by source paper "fedloc federated learning framework for cooperative localization and location data processing"):
  with members:
    - "human motion trajectory prediction a survey"
    - "towards federated learning at scale system design"
    - "contextualized spatial temporal network for taxi origin destination demand prediction"
    - "on exact computation with an infinitely wide neural net"
    - "wireless traffic prediction with scalable gaussian process framework algorithms and verification"
    - "federated machine learning concept and applications"
    - "federated learning for ultra reliable low latency v2v communications"
    - "federated learning strategies for improving communication efficiency"

Connected through hyperedge HE-2 (induced by source paper "asymmetric residual neural network for accurate human activity recognition"):
  with members:
    - "deep attention guided hashing"
    - "dfternet towards 2 bit dynamic fusion networks for accurate human activity recognition"
    - "slowfast networks for video recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "non local neural networks"
    - "compression artifacts removal using convolutional neural networks"
    - "a theory of local learning the learning channel and the optimality of backpropagation"
    - "spatiotemporal residual networks for video action recognition"

Connected through hyperedge HE-3 (induced by source paper "a convolutional neural network for user identification based on motion sensors"):
  with members:
    - "local learning with deep and handcrafted features for facial expression recognition"
    - "sequential keystroke behavioral biometrics for mobile user identification via multi view deep learning"
    - "attention is all you need"
    - "how hard can it be estimating the difficulty of visual search in an image"
    - "tensorflow a system for large scale machine learning"
    - "you only look once unified real time object detection"
    - "deep residual learning for image recognition"
    - "hmog new behavioral biometric features for continuous authentication of smartphone users"

Connected through hyperedge HE-4 (induced by source paper "mobile sensor data anonymization"):
  with members:
    - "replacement autoencoder a privacy preserving algorithm for sensory data analysis"
    - "optimal utility privacy trade off with the total variation distance as the privacy measure"
    - "distributed one class learning"
    - "protecting sensory data against sensitive inferences"
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "privacy preserving adversarial networks"
    - "context aware generative adversarial privacy"
    - "privacy preserving personal model training"

Connected through hyperedge HE-5 (induced by source paper "movement science needs different pose tracking algorithms"):
  with members:
    - "self supervised learning of 3d human pose using multi view geometry"
    - "deep high resolution representation learning for human pose estimation"
    - "parsing r cnn for instance level human analysis"
    - "an integral pose regression system for the eccv2018 posetrack challenge"
    - "densepose dense human pose estimation in the wild"
    - "learning feature pyramids for human pose estimation"
    - "unsupervised learning of depth and ego motion from video"
    - "posetrack a benchmark for human pose estimation and tracking"

Connected through hyperedge HE-6 (induced by source paper "privacy and utility preserving sensor data transformations"):
  with members:
    - "mobile sensor data anonymization"
    - "towards federated learning at scale system design"
    - "replacement autoencoder a privacy preserving algorithm for sensory data analysis"
    - "protecting sensory data against sensitive inferences"
    - "context aware generative adversarial privacy"
    - "prochlo strong privacy for analytics in the crowd"
    - "censoring representations with an adversary"
    - "privacy against statistical inference"

Connected through hyperedge HE-7 (induced by source paper "actions speak louder than pass words passive authentication of smartphone users via deep temporal features"):
  with members:
    - "multilock mobile active authentication based on multiple biometric and behavioral patterns"
    - "active authentication on mobile devices via stylometry application usage web browsing and gps location"
    - "active user authentication for smartphones a challenge data set and benchmark results"
    - "hmog new behavioral biometric features for continuous authentication of smartphone users"
    - "adam a method for stochastic optimization"
    - "an hmm based behavior modeling approach for continuous mobile authentication"
    - "touchalytics on the applicability of touchscreen input as a behavioral biometric for continuous authentication"

Connected through hyperedge HE-8 (induced by source paper "multimodal feature fusion for cnn based gait recognition an empirical comparison"):
  with members:
    - "end to end incremental learning"
    - "initialization strategies of spatio temporal convolutional neural networks"
    - "deep residual learning for image recognition"
    - "multimodal deep learning for robust rgb d object recognition"
    - "action recognition with trajectory pooled deep convolutional descriptors"
    - "very deep convolutional networks for large scale image recognition"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "matconvnet convolutional neural networks for matlab"

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.LG(Machine Learning)