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 multi instance transfer learning"
  Abstract: We present a new approach for transferring knowledge from groups to individuals that comprise them. We evaluate our method in text, by inferring the ratings of individual sentences using full-review ratings. This approach, which combines ideas from transfer learning, deep learning and multi-instance learning, reduces the need for laborious human labelling of fine-grained data when abundant labels are available at the group level.

Connected through hyperedge HE-1 (induced by source paper "heterogeneous knowledge transfer in video emotion recognition attribution and summarization"):
  with members:
    - "robust subjective visual property prediction from crowdsourced pairwise labels"
    - "transductive multi view zero shot learning"
    - "robust image sentiment analysis using progressively trained and domain transferred deep networks"
    - "multi view metric learning for multi view video summarization"
    - "going deeper with convolutions"
    - "return of the devil in the details delving deep into convolutional nets"
    - "deepsentibank visual sentiment concept classification with deep convolutional neural networks"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-2 (induced by source paper "multiple instance learning a survey of problem characteristics and applications"):
  with members:
    - "feature learning from spectrograms for assessment of personality traits"
    - "risk minimization framework for multiple instance learning from positive and unlabeled bags"
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "weakly supervised scalable audio content analysis"
    - "label stability in multiple instance learning"
    - "characterizing multiple instance datasets"
    - "a multi scale multiple instance video description network"
    - "detector discovery in the wild joint multiple instance and representation learning"

Connected through hyperedge HE-3 (induced by source paper "word embeddings and their use in sentence classification tasks"):
  with members:
    - "a latent variable model approach to pmi based word embeddings"
    - "rnn fisher vectors for action recognition and image annotation"
    - "a sensitivity analysis of and practitioners guide to convolutional neural networks for sentence classification"
    - "adam a method for stochastic optimization"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "caffe convolutional architecture for fast feature embedding"
    - "convolutional neural networks for sentence classification"
    - "word2vec explained deriving mikolov et al s negative sampling word embedding method"

Connected through hyperedge HE-4 (induced by source paper "netvlad cnn architecture for weakly supervised place recognition"):
  with members:
    - "lift learned invariant feature transform"
    - "bags of local convolutional features for scalable instance search"
    - "deep image retrieval learning global representations for image search"
    - "planet photo geolocation with convolutional neural networks"
    - "cnn image retrieval learns from bow unsupervised fine tuning with hard examples"
    - "bilinear cnn models for fine grained visual recognition"
    - "cross dimensional weighting for aggregated deep convolutional features"
    - "facenet a unified embedding for face recognition and clustering"

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)