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: "openxbow introducing the passau open source crossmodal bag of words toolkit"
  Abstract: We introduce openXBOW, an open-source toolkit for the generation of bag-of-words (BoW) representations from multimodal input. In the BoW principle, word histograms were first used as features in document classification, but the idea was and can easily be adapted to, e.g., acoustic or visual low-level descriptors, introducing a prior step of vector quantisation. The openXBOW toolkit supports arbitrary numeric input features and text input and concatenates computed subbags to a final bag. It provides a variety of extensions and options. To our knowledge, openXBOW is the first publicly available toolkit for the generation of crossmodal bags-of-words. The capabilities of the tool are exemplified in two sample scenarios: time-continuous speech-based emotion recognition and sentiment analysis in tweets where improved results over other feature representation forms were observed.

Connected through hyperedge HE-1 (induced by source paper "snore gans improving automatic snore sound classification with synthesized data"):
  with members:
    - "generative adversarial networks an overview"
    - "adversarial training in affective computing and sentiment analysis recent advances and perspectives"
    - "data augmentation in emotion classification using generative adversarial networks"
    - "learning to fuse music genres with generative adversarial dual learning"
    - "learning audio sequence representations for acoustic event classification"
    - "segan speech enhancement generative adversarial network"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "end to end multimodal emotion recognition using deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "avec 2019 workshop and challenge state of mind detecting depression with ai and cross cultural affect recognition"):
  with members:
    - "sewa db a rich database for audio visual emotion and sentiment research in the wild"
    - "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond"
    - "on many to many mapping between concordance correlation coefficient and mean square error"
    - "topic modeling based multi modal depression detection"
    - "large scale analysis of counseling conversations an application of natural language processing to mental health"
    - "avec 2016 depression mood and emotion recognition workshop and challenge"
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "multi level attention network using text audio and video for depression prediction"):
  with members:
    - "inverse boosting pruning trees for depression detection on twitter"
    - "the verbal and non verbal signals of depression combining acoustics text and visuals for estimating depression level"
    - "universal sentence encoder"
    - "attention is all you need"
    - "fera 2017 addressing head pose in the third facial expression recognition and analysis challenge"
    - "avec 2016 depression mood and emotion recognition workshop and challenge"

Connected through hyperedge HE-4 (induced by source paper "n hans introducing the augsburg neuro holistic audio enhancement system"):
  with members:
    - "voxceleb2 deep speaker recognition"
    - "audeep unsupervised learning of representations from audio with deep recurrent neural networks"
    - "supervised speech separation based on deep learning an overview"
    - "voxceleb a large scale speaker identification dataset"
    - "deep clustering discriminative embeddings for segmentation and separation"

Connected through hyperedge HE-5 (induced by source paper "deep neural baselines for computational paralinguistics"):
  with members:
    - "deep learning for human affect recognition insights and new developments"
    - "learning transferable architectures for scalable image recognition"
    - "audeep unsupervised learning of representations from audio with deep recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "a theory of sequence indexing and working memory in recurrent neural networks"):
  with members:
    - "integer echo state networks hyperdimensional reservoir computing"
    - "associative long short term memory"
    - "distributed sequence memory of multidimensional inputs in recurrent networks"
    - "representing objects relations and sequences"
    - "short term memory capacity in networks via the restricted isometry property"
    - "quantum computation via sparse distributed representation"
    - "vector symbolic architectures answer jackendoff s challenges for cognitive neuroscience"

Connected through hyperedge HE-7 (induced by source paper "audeep unsupervised learning of representations from audio with deep recurrent neural networks"):
  with members:
    - "soundnet learning sound representations from unlabeled video"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-8 (induced by source paper "continuous multimodal emotion recognition approach for avec 2017"):
  with members:
    - "deep residual learning for image recognition"
    - "matconvnet convolutional neural networks for matlab"

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)