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: "bag of words representation for biomedical time series classification"
  Abstract: Abstract   Automatic analysis of biomedical time series such as electroencephalogram (EEG) and electrocardiographic (ECG) signals has attracted great interest in the community of biomedical engineering due to its important applications in medicine. In this work, a simple yet effective bag-of-words representation that is originally developed for text document analysis is extended for biomedical time series representation. In particular, similar to the bag-of-words model used in text document domain, the proposed method treats a time series as a text document and extracts local segments from the time series as words. The biomedical time series is then represented as a histogram of codewords, each entry of which is the count of a codeword appeared in the time series. Although the temporal order of the local segments is ignored, the bag-of-words representation is able to capture high-level structural information because both local and global structural information are well utilized. The performance of the bag-of-words model is validated on three datasets extracted from real EEG and ECG signals. The experimental results demonstrate that the proposed method is not only insensitive to parameters of the bag-of-words model such as local segment length and codebook size, but also robust to noise.

Connected through hyperedge HE-1 (induced by source paper "ts chief a scalable and accurate forest algorithm for time series classification"):
  with members:
    - "scalable dictionary classifiers for time series classification"
    - "deep learning for time series classification a review"
    - "proximity forest an effective and scalable distance based classifier for time series"
    - "deep learning for sensor based activity recognition a survey"
    - "temporal convolutional neural network for the classification of satellite image time series"
    - "from bop to boss and beyond time series classification with dictionary based classifiers"
    - "scalable and accurate deep learning for electronic health records"
    - "the ucr time series archive"

Connected through hyperedge HE-2 (induced by source paper "bag of recurrence patterns representation for time series classification"):
  with members:
    - "classification of time series images using deep convolutional neural networks"
    - "dense bag of temporal sift words for time series classification"
    - "scalable classification of repetitive time series through frequencies of local polynomials"
    - "classifiers with a reject option for early time series classification"
    - "ecoc based training of neural networks for face recognition"

Connected through hyperedge HE-3 (induced by source paper "capturing the symptoms of malicious code in electronic documents by file s entropy signal combined with machine learning"):
  with members:
    - "henet a deep learning approach on intel circledr processor trace for effective exploit detection"
    - "evading classifiers by morphing in the dark"
    - "wavelet decomposition of software entropy reveals symptoms of malicious code"

Connected through hyperedge HE-4 (induced by source paper "a comparative study of cnn bovw and lbp for classification of histopathological images"):
  with members:
    - "how transferable are features in deep neural networks"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"
    - "using a bag of words for automatic medical image annotation with a latent semantic"
    - "glcm based chi square histogram distance for automatic detection of defects on patterned textures"

Connected through hyperedge HE-5 (induced by source paper "classification of time series images using deep convolutional neural networks"):
  with members:
    - "multi scale convolutional neural networks for time series classification"
    - "very deep convolutional networks for large scale image recognition"
    - "speech recognition with deep recurrent neural networks"
    - "ecoc based training of neural networks for face recognition"

Connected through hyperedge HE-6 (induced by source paper "encoding the local connectivity patterns of fmri for cognitive state classification"):
  with members:
    - "joint recognition and segmentation of actions via probabilistic integration of spatio temporal fisher vectors"
    - "learning discriminative bayesian networks from high dimensional continuous neuroimaging data"
    - "vectors of locally aggregated centers for compact video representation"
    - "encoding high dimensional local features by sparse coding based fisher vectors"
    - "mesh learning for classifying cognitive processes"

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)