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: "deep belief networks used on high resolution multichannel electroencephalography data for seizure detection"
  Abstract: Ubiquitous bio-sensing for personalized health monitoring is slowly becoming a reality with the increasing availability of small, diverse, robust, high fidelity sensors. This oncoming flood of data begs the question of how we will extract useful information from it. In this paper we explore the use of a variety of representations and machine learning algorithms applied to the task of seizure detection in high resolution, multichannel EEG data. We explore classification accuracy, computational complexity and memory requirements with a view toward understanding which approaches are most suitable for such tasks as the number of people involved and the amount of data they produce grows to be quite large. In particular, we show that layered learning approaches such as Deep Belief Networks excel along these dimensions.

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 "seizurenet multi spectral deep feature learning for seizure type classification"):
  with members:
    - "machine learning for seizure type classification setting the benchmark"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "learning robust features using deep learning for automatic seizure detection"
    - "densely connected convolutional networks"
    - "detecting epileptic seizures from eeg data using neural networks"

Connected through hyperedge HE-3 (induced by source paper "deep learning in bioinformatics"):
  with members:
    - "brain tumor segmentation with deep neural networks"
    - "deep motif visualizing genomic sequence classifications"
    - "deepmirgene deep neural network based precursor microrna prediction"
    - "comparative study of deep learning software frameworks"
    - "deeptarget end to end learning framework for microrna target prediction using deep recurrent neural networks"
    - "deepspark spark based deep learning supporting asynchronous updates and caffe compatibility"
    - "deepspark a spark based distributed deep learning framework for commodity clusters"
    - "improving computer aided detection using convolutional neural networks and random view aggregation"

Connected through hyperedge HE-4 (induced by source paper "keypoint density based region proposal for fine grained object detection and classification using regions with convolutional neural network features"):
  with members:
    - "fast r cnn"
    - "very deep convolutional networks for large scale image recognition"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

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)