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 learning for extracting protein protein interactions from biomedical literature"
  Abstract: State-of-the-art methods for protein-protein interaction (PPI) extraction are primarily feature-based or kernel-based by leveraging lexical and syntactic information. But how to incorporate such knowledge in the recent deep learning methods remains an open question. In this paper, we propose a multichannel dependency-based convolutional neural network model (McDepCNN). It applies one channel to the embedding vector of each word in the sentence, and another channel to the embedding vector of the head of the corresponding word. Therefore, the model can use richer information obtained from different channels. Experiments on two public benchmarking datasets, AIMed and BioInfer, demonstrate that McDepCNN compares favorably to the state-of-the-art rich-feature and single-kernel based methods. In addition, McDepCNN achieves 24.4% relative improvement in F1-score over the state-of-the-art methods on cross-corpus evaluation and 12% improvement in F1-score over kernel-based methods on "difficult" instances. These results suggest that McDepCNN generalizes more easily over different corpora, and is capable of capturing long distance features in the sentences.

Connected through hyperedge HE-1 (induced by source paper "a self attention based deep learning method for lesion attribute detection from ct reports"):
  with members:
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "attention guided curriculum learning for weakly supervised classification and localization of thoracic diseases on chest radiographs"
    - "biosentvec creating sentence embeddings for biomedical texts"
    - "attention is all you need"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "relation extraction from clinical texts using domain invariant convolutional neural network"
    - "show attend and tell neural image caption generation with visual attention"
    - "interleaved text image deep mining on a large scale radiology database for automated image interpretation"

Connected through hyperedge HE-2 (induced by source paper "using natural language processing techniques to extract information on the properties and functionalities of energetic materials from large text corpora"):
  with members:
    - "a survey of word embeddings evaluation methods"
    - "drug drug interaction extraction via recurrent neural network with multiple attention layers"
    - "exploiting task oriented resources to learn word embeddings for clinical abbreviation expansion"
    - "growth rates of modern science a bibliometric analysis based on the number of publications and cited references"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "nearly unsupervised hashcode representations for relation extraction"):
  with members:
    - "exploring semi supervised variational autoencoders for biomedical relation extraction"
    - "stochastic learning of nonstationary kernels for natural language modeling"
    - "kernelized hashcode representations for relation extraction"
    - "extracting biomolecular interactions using semantic parsing of biomedical text"
    - "semi supervised learning with deep generative models"

Connected through hyperedge HE-4 (induced by source paper "feature assisted bi directional lstm model for protein protein interaction identification from biomedical texts"):
  with members:
    - "end to end relation extraction using lstms on sequences and tree structures"
    - "extracting biomolecular interactions using semantic parsing of biomedical text"
    - "a dependency based neural network for relation classification"
    - "adam a method for stochastic optimization"
    - "generating sequences with recurrent neural networks"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-5 (induced by source paper "leveraging prior knowledge for protein protein interaction extraction with memory network"):
  with members:
    - "aspect level sentiment classification with deep memory network"
    - "end to end memory networks"
    - "relation classification via recurrent neural network"
    - "adam a method for stochastic optimization"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "biosentvec creating sentence embeddings for biomedical texts"):
  with members:
    - "universal sentence encoder"
    - "medsts a resource for clinical semantic textual similarity"
    - "distributed representations of sentences and documents"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-7 (induced by source paper "chemical protein relation extraction with ensembles of svm cnn and rnn models"):
  with members:
    - "classifying relations by ranking with convolutional neural networks"
    - "adam a method for stochastic optimization"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-8 (induced by source paper "kernelized hashcode representations for relation extraction"):
  with members:
    - "learning to explain an information theoretic perspective on model interpretation"
    - "deep variational information bottleneck"
    - "a survey on learning to hash"
    - "orthogonal random features"
    - "generalized exponential concentration inequality for r enyi divergence estimation"
    - "learning structural kernels for natural language processing"
    - "adaptive estimation of shannon entropy"
    - "extracting biomolecular interactions using semantic parsing of biomedical text"

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.CL(Computation and Language)