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: "multi modality sensor data classification with selective attention"
  Abstract: Multimodal wearable sensor data classification plays an important role in ubiquitous computing and has a wide range of applications in scenarios from healthcare to entertainment. However, most existing work in this field employs domain-specific approaches and is thus ineffective in complex sit- uations where multi-modality sensor data are col- lected. Moreover, the wearable sensor data are less informative than the conventional data such as texts or images. In this paper, to improve the adapt- ability of such classification methods across differ- ent application domains, we turn this classification task into a game and apply a deep reinforcement learning scheme to deal with complex situations dynamically. Additionally, we introduce a selective attention mechanism into the reinforcement learn- ing scheme to focus on the crucial dimensions of the data. This mechanism helps to capture extra information from the signal and thus it is able to significantly improve the discriminative power of the classifier. We carry out several experiments on three wearable sensor datasets and demonstrate the competitive performance of the proposed approach compared to several state-of-the-art baselines.

Connected through hyperedge HE-1 (induced by source paper "mdldroid a chainsgd reduce approach to mobile deep learning for personal mobile sensing"):
  with members:
    - "a distributed synchronous sgd algorithm with global top k sparsification for low bandwidth networks"
    - "deep learning on mobile devices a review"
    - "layered sgd a decentralized and synchronous sgd algorithm for scalable deep neural network training"
    - "towards federated learning at scale system design"
    - "analyzing federated learning through an adversarial lens"
    - "deep learning towards mobile applications"
    - "edge intelligence on demand deep learning model co inference with device edge synergy"
    - "deep reinforcement learning for multi agent systems a review of challenges solutions and applications"

Connected through hyperedge HE-2 (induced by source paper "adversarial variational embedding for robust semi supervised learning"):
  with members:
    - "interpretable parallel recurrent neural networks with convolutional attentions for multi modality activity modeling"
    - "deep learning with convolutional neural networks for decoding and visualization of eeg pathology"
    - "neural factorization machines for sparse predictive analytics"
    - "cvae gan fine grained image generation through asymmetric training"
    - "objective evaluation metrics for automatic classification of eeg events"
    - "variational autoencoders for semi supervised text classification"
    - "improved techniques for training gans"
    - "an uncertain future forecasting from static images using variational autoencoders"

Connected through hyperedge HE-3 (induced by source paper "multi agent attentional activity recognition"):
  with members:
    - "interpretable parallel recurrent neural networks with convolutional attentions for multi modality activity modeling"
    - "ensembles of deep lstm learners for activity recognition using wearables"
    - "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks"
    - "recurrent models of visual attention"

Connected through hyperedge HE-4 (induced by source paper "know your mind adaptive brain signal classification with reinforced attentive convolutional neural networks"):
  with members:
    - "a survey on deep learning based brain computer interface recent advances and new frontiers"
    - "adversarial variational embedding for robust semi supervised learning"
    - "automatic analysis of eegs using big data and hybrid deep learning architectures"
    - "mindid person identification from brain waves through attention based recurrent neural network"
    - "eeg based intention recognition from spatio temporal representations via cascade and parallel convolutional recurrent neural networks"
    - "converting your thoughts to texts enabling brain typing via deep feature learning of eeg signals"
    - "intent recognition in smart living through deep recurrent neural networks"
    - "objective evaluation metrics for automatic classification of eeg events"

Connected through hyperedge HE-5 (induced by source paper "mindid person identification from brain waves through attention based recurrent neural network"):
  with members:
    - "interpretable parallel recurrent neural networks with convolutional attentions for multi modality activity modeling"
    - "multi person brain activity recognition via comprehensive eeg signal analysis"
    - "xgboost a scalable tree boosting system"
    - "survey on the attention based rnn model and its applications in computer vision"
    - "effective approaches to attention based neural machine translation"
    - "end to end attention based large vocabulary speech recognition"
    - "adam a method for stochastic optimization"
    - "multiple object recognition with visual attention"

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)