human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "seizurenet multi spectral deep feature learning for seizure type classification"):
  Abstract: Automatic classification of epliptic seizure types in EEG datacould enable more precise diagnosis and efficient manage-ment of the disease. Automatic seizure type classificationusing clinical electroencephalograms (EEGs) is challengingdue to factors such as low signal to noise ratios, signal arte-facts, high variance in the seizure semiology among individ-ual epileptic patients, and limited availability of clinical this http URL overcome these challenges, in this paper, we present adeep learning based framework which learns multi-spectralfeature embeddings using multiple CNN models in an ensem-ble architecture for accurate cross-patient seizure type clas-sification. Experiments on the recently released TUH EEGSeizure Corpus show that our multi-spectral dense featurelearning produces a weighted f1 score of 0.98 for seizure typeclassification setting new benchmarks on the dataset.

Includes members:
  Member "machine learning for seizure type classification setting the benchmark":
    also connected through other hyperedges:
      (none)
  Member "mobilenets efficient convolutional neural networks for mobile vision applications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "deep belief networks used on high resolution multichannel electroencephalography data for seizure detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on deep learning based brain computer interface recent advances and new frontiers")
  Member "learning robust features using deep learning for automatic seizure detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "temporal graph convolutional networks for automatic seizure detection")
  Member "densely connected convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "lossless compression of deep neural networks")
  Member "detecting epileptic seizures from eeg data using neural networks":
    also connected through other hyperedges:
      (none)

Question: Please tell me which class the source paper 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)