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: "transforming sensor data to the image domain for deep learning an application to footstep detection"
  Abstract: Convolutional Neural Networks (CNNs) have become the state-of-the-art in various computer vision tasks, but they are still premature for most sensor data, especially in pervasive and wearable computing. A major reason for this is the limited amount of annotated training data. In this paper, we propose the idea of leveraging the discriminative power of pre-trained deep CNNs on 2-dimensional sensor data by transforming the sensor modality to the visual domain. By three proposed strategies, 2D sensor output is converted into pressure distribution imageries. Then we utilize a pre-trained CNN for transfer learning on the converted imagery data. We evaluate our method on a gait dataset of floor surface pressure mapping. We obtain a classification accuracy of 87.66%, which outperforms the conventional machine learning methods by over 10%.

Connected through hyperedge HE-1 (induced by source paper "a comprehensive study of imagenet pre training for historical document image analysis"):
  with members:
    - "labeling cutting grouping an efficient text line segmentation method for medieval manuscripts"
    - "do better imagenet models transfer better"
    - "offline signature verification by combining graph edit distance and triplet networks"
    - "deep learning for classical japanese literature"
    - "deepdiva a highly functional python framework for reproducible experiments"
    - "identifying cross depicted historical motifs"
    - "open evaluation tool for layout analysis of document images"
    - "historical document image segmentation with lda initialized deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "domain translation with conditional gans from depth to rgb face to face"):
  with members:
    - "from depth data to head pose estimation a siamese approach"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "image restoration using autoencoding priors"
    - "deep head pose estimation from depth data for in car automotive applications"
    - "face from depth for head pose estimation on depth images"
    - "generative adversarial models for people attribute recognition in surveillance"
    - "poseidon face from depth for driver pose estimation"
    - "image restoration using convolutional auto encoders with symmetric skip connections"

Connected through hyperedge HE-3 (induced by source paper "deep learning for sensor based activity recognition a survey"):
  with members:
    - "balanced distribution adaptation for transfer learning"
    - "a deep learning framework using passive wifi sensing for respiration monitoring"
    - "ensembles of deep lstm learners for activity recognition using wearables"
    - "sensor based gait parameter extraction with deep convolutional neural networks"
    - "deepsense a unified deep learning framework for time series mobile sensing data processing"
    - "label free supervision of neural networks with physics and domain knowledge"
    - "deep convolutional and recurrent models for human activity recognition using wearables"
    - "deep recurrent neural network for mobile human activity recognition with high throughput"

Connected through hyperedge HE-4 (induced by source paper "smart device based initial movement detection of cyclists using convolutional neuronal networks"):
  with members:
    - "early start intention detection of cyclists using motion history images and a deep residual network"
    - "detecting intentions of vulnerable road users based on collective intelligence"
    - "cooperative starting movement detection of cyclists using convolutional neural networks and a boosted stacking ensemble"
    - "identity mappings in deep residual networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "imagenet large scale visual recognition challenge"

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)