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: "improving one shot learning through fusing side information"
  Abstract: Deep Neural Networks (DNNs) often struggle with one-shot learning where we have only one or a few labeled training examples per category. In this paper, we argue that by using side information, we may compensate the missing information across classes. We introduce two statistical approaches for fusing side information into data representation learning to improve one-shot learning. First, we propose to enforce the statistical dependency between data representations and multiple types of side information. Second, we introduce an attention mechanism to efficiently treat examples belonging to the 'lots-of-examples' classes as quasi-samples (additional training samples) for 'one-example' classes. We empirically show that our learning architecture improves over traditional softmax regression networks as well as state-of-the-art attentional regression networks on one-shot recognition tasks.

Connected through hyperedge HE-1 (induced by source paper "few shot classification in named entity recognition task"):
  with members:
    - "sequence labeling a practical approach"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "label aware double transfer learning for cross specialty medical named entity recognition"
    - "learning how to self learn enhancing self training using neural reinforcement learning"
    - "neural cross lingual named entity recognition with minimal resources"
    - "deep active learning for named entity recognition"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-2 (induced by source paper "modeling the biological pathology continuum with hsic regularized wasserstein auto encoders"):
  with members:
    - "isolating sources of disentanglement in variational autoencoders"
    - "information constraints on auto encoding variational bayes"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "learning factorized multimodal representations"):
  with members:
    - "multimodal sentiment analysis with word level fusion and reinforcement learning"
    - "multi attention recurrent network for human communication comprehension"
    - "dependency bottleneck in auto encoding architectures an empirical study"
    - "multimodal local global ranking fusion for emotion recognition"
    - "efficient low rank multimodal fusion with modality specific factors"
    - "seq2seq2sentiment multimodal sequence to sequence models for sentiment analysis"
    - "memory fusion network for multi view sequential learning"
    - "tensor fusion network for multimodal sentiment analysis"

Connected through hyperedge HE-4 (induced by source paper "generalized zero and few shot learning via aligned variational autoencoders"):
  with members:
    - "low shot learning from imaginary data"
    - "style transfer from non parallel text by cross alignment"
    - "an empirical evaluation of visual question answering for novel objects"
    - "learning robust visual semantic embeddings"
    - "a generative model for zero shot learning using conditional variational autoencoders"
    - "prototypical networks for few shot learning"
    - "adversarial discriminative domain adaptation"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "rapid adaptation with conditionally shifted neurons"):
  with members:
    - "modulating early visual processing by language"
    - "learning algorithms for active learning"
    - "film visual reasoning with a general conditioning layer"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "learning to learn by gradient descent by gradient descent"
    - "overcoming catastrophic forgetting in neural networks"
    - "matching networks for one shot learning"
    - "towards biologically plausible deep learning"

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)