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: "cloud based deep learning of big eeg data for epileptic seizure prediction"
  Abstract: Developing a Brain-Computer Interface~(BCI) for seizure prediction can help epileptic patients have a better quality of life. However, there are many difficulties and challenges in developing such a system as a real-life support for patients. Because of the nonstationary nature of EEG signals, normal and seizure patterns vary across different patients. Thus, finding a group of manually extracted features for the prediction task is not practical. Moreover, when using implanted electrodes for brain recording massive amounts of data are produced. This big data calls for the need for safe storage and high computational resources for real-time processing. To address these challenges, a cloud-based BCI system for the analysis of this big EEG data is presented. First, a dimensionality-reduction technique is developed to increase classification accuracy as well as to decrease the communication bandwidth and computation time. Second, following a deep-learning approach, a stacked autoencoder is trained in two steps for unsupervised feature extraction and classification. Third, a cloud-computing solution is proposed for real-time analysis of big EEG data. The results on a benchmark clinical dataset illustrate the superiority of the proposed patient-specific BCI as an alternative method and its expected usefulness in real-life support of epilepsy patients.

Connected through hyperedge HE-1 (induced by source paper "a survey on deep learning based brain computer interface recent advances and new frontiers"):
  with members:
    - "deep learning based electroencephalography analysis a systematic review"
    - "adversarial variational embedding for robust semi supervised learning"
    - "spatial temporal recurrent neural network for emotion recognition"
    - "an automated system for epilepsy detection using eeg brain signals based on deep learning approach"
    - "on the classification of ssvep based dry eeg signals via convolutional neural networks"
    - "eegnet a compact convolutional neural network for eeg based brain computer interfaces"
    - "compact convolutional neural networks for classification of asynchronous steady state visual evoked potentials"
    - "hierarchical internal representation of spectral features in deep convolutional networks trained for eeg decoding"

Connected through hyperedge HE-2 (induced by source paper "boosting occluded image classification via subspace decomposition based estimation of deep features"):
  with members:
    - "adversarially approximated autoencoder for image generation and manipulation"
    - "facelet bank for fast portrait manipulation"
    - "mdcn multi scale deep inception convolutional neural networks for efficient object detection"
    - "learning depth from single images with deep neural network embedding focal length"
    - "generative image inpainting with contextual attention"
    - "network dissection quantifying interpretability of deep visual representations"
    - "perceptual generative adversarial networks for small object detection"
    - "cvae gan fine grained image generation through asymmetric training"

Connected through hyperedge HE-3 (induced by source paper "low complexity secure code lcsc design for big data in cloud storage systems"):
  with members:
    - "secure and private cloud storage systems with random linear fountain codes"
    - "a secure approach for caching contents in wireless ad hoc networks"
    - "multihop caching aided coded multicasting for the next generation of cellular networks"
    - "secure repairable fountain codes"
    - "throughput analysis of decentralized coded content caching in cellular networks"
    - "capacity of cellular networks with femtocache"
    - "optimal locally repairable and secure codes for distributed storage systems"
    - "weakly secure regenerating codes for distributed storage"

Connected through hyperedge HE-4 (induced by source paper "secure and private cloud storage systems with random linear fountain codes"):
  with members:
    - "private information retrieval from mds coded data in distributed storage systems"
    - "a secure approach for caching contents in wireless ad hoc networks"
    - "multihop caching aided coded multicasting for the next generation of cellular networks"
    - "optimizing pilot overhead for ultra reliable short packet transmission"
    - "secure repairable fountain codes"
    - "distributed vector estimation for power and bandwidth constrained wireless sensor networks"
    - "throughput analysis of decentralized coded content caching in cellular networks"
    - "capacity of cellular networks with femtocache"

Connected through hyperedge HE-5 (induced by source paper "non intrusive energy disaggregation using non negative matrix factorization with sum to k constraint"):
  with members:
    - "innovation pursuit a new approach to subspace clustering"
    - "feature encoding in band limited distributed surveillance systems"
    - "does disaggregated electricity feedback reduce domestic electricity consumption a systematic review of the literature"
    - "missing spectrum data recovery in cognitive radio networks using piecewise constant nonnegative matrix factorization"
    - "sparse modeling for image and vision processing"
    - "an empirical investigation of v i trajectory based load signatures for non intrusive load monitoring"

Connected through hyperedge HE-6 (induced by source paper "multi view task driven recognition in visual sensor networks"):
  with members:
    - "high dimensional low rank plus sparse matrix decomposition"
    - "control allocation for wide area coordinated damping"
    - "screen content image segmentation using robust regression and sparse decomposition"
    - "feature encoding in band limited distributed surveillance systems"
    - "quality based multimodal classification using tree structured sparsity"

Connected through hyperedge HE-7 (induced by source paper "understanding the computational requirements of virtualized baseband units using a programmable cloud radio access network testbed"):
  with members:
    - "octopus a cooperative hierarchical caching strategy for cloud radio access networks"
    - "dynamic radio cooperation for downlink cloud rans with computing resource sharing"

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)