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: "mobile sensor data anonymization"
  Abstract: Motion sensors such as accelerometers and gyroscopes measure the instant acceleration and rotation of a device, in three dimensions. Raw data streams from motion sensors embedded in portable and wearable devices may reveal private information about users without their awareness. For example, motion data might disclose the weight or gender of a user, or enable their re-identification. To address this problem, we propose an on-device transformation of sensor data to be shared for specific applications, such as monitoring selected daily activities, without revealing information that enables user identification. We formulate the anonymization problem using an information-theoretic approach and propose a new multi-objective loss function for training deep autoencoders. This loss function helps minimizing user-identity information as well as data distortion to preserve the application-specific utility. The training process regulates the encoder to disregard user-identifiable patterns and tunes the decoder to shape the output independently of users in the training set. The trained autoencoder can be deployed on a mobile or wearable device to anonymize sensor data even for users who are not included in the training dataset. Data from 24 users transformed by the proposed anonymizing autoencoder lead to a promising trade-off between utility and privacy, with an accuracy for activity recognition above 92% and an accuracy for user identification below 7%.

Connected through hyperedge HE-1 (induced by source paper "quotient two party secure neural network training and prediction"):
  with members:
    - "on the convergence of adam and beyond"
    - "xonn xnor based oblivious deep neural network inference"
    - "training and inference with integers in deep neural networks"
    - "high accuracy low precision training"
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "understanding batch normalization"
    - "tapas tricks to accelerate encrypted prediction as a service"
    - "signsgd compressed optimisation for non convex problems"

Connected through hyperedge HE-2 (induced by source paper "privacy and utility preserving sensor data transformations"):
  with members:
    - "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"
    - "learning human identity from motion patterns"
    - "censoring representations with an adversary"
    - "privacy against statistical inference"

Connected through hyperedge HE-3 (induced by source paper "finding dory in the crowd detecting social interactions using multi modal mobile sensing"):
  with members:
    - "replacement autoencoder a privacy preserving algorithm for sensory data analysis"
    - "sensingkit evaluating the sensor power consumption in ios devices"
    - "xgboost a scalable tree boosting system"
    - "learning human identity from motion patterns"
    - "tracking serendipitous interactions how individual cultures shape the office"

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)