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: "drug drug interaction extraction from biomedical text using long short term memory network"
  Abstract: Simultaneous administration of multiple drugs can have synergistic or antagonistic effects as one drug can affect activities of other drugs. Synergistic effects lead to improved therapeutic outcomes, whereas, antagonistic effects can be life-threatening, may lead to increased healthcare cost, or may even cause death. Thus identification of unknown drug-drug interaction (DDI) is an important concern for efficient and effective healthcare. Although multiple resources for DDI exist, they are often unable to keep pace with rich amount of information available in fast growing biomedical texts. Most existing methods model DDI extraction from text as a classification problem and mainly rely on handcrafted features. Some of these features further depend on domain specific tools. Recently neural network models using latent features have been shown to give similar or better performance than the other existing models dependent on handcrafted features. In this paper, we present three models namely, {\it B-LSTM}, {\it AB-LSTM} and {\it Joint AB-LSTM} based on long short-term memory (LSTM) network. All three models utilize word and position embedding as latent features and thus do not rely on explicit feature engineering. Further use of bidirectional long short-term memory (Bi-LSTM) networks allow implicit feature extraction from the whole sentence. The two models, {\it AB-LSTM} and {\it Joint AB-LSTM} also use attentive pooling in the output of Bi-LSTM layer to assign weights to features. Our experimental results on the SemEval-2013 DDI extraction dataset show that the {\it Joint AB-LSTM} model outperforms all the existing methods, including those relying on handcrafted features. The other two proposed LSTM models also perform competitively with state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "classifying medical relations in clinical text via convolutional neural networks"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "relation extraction from clinical texts using domain invariant convolutional neural network"
    - "improved relation classification by deep recurrent neural networks with data augmentation"
    - "end to end relation extraction using lstms on sequences and tree structures"
    - "classifying relations by ranking with convolutional neural networks"
    - "local translation prediction with global sentence representation"
    - "classifying relations via long short term memory networks along shortest dependency path"
    - "task oriented learning of word embeddings for semantic relation classification"

Connected through hyperedge HE-2 (induced by source paper "automatic severity classification of coronary artery disease via recurrent capsule network"):
  with members:
    - "a walk based model on entity graphs for relation extraction"
    - "dsgan generative adversarial training for distant supervision relation extraction"
    - "robust distant supervision relation extraction via deep reinforcement learning"
    - "incorporating dictionaries into deep neural networks for the chinese clinical named entity recognition"
    - "reinforcement learning for relation classification from noisy data"
    - "joint extraction of entities and relations based on a novel tagging scheme"
    - "dynamic routing between capsules"
    - "end to end relation extraction using lstms on sequences and tree structures"

Connected through hyperedge HE-3 (induced by source paper "enhancing drug drug interaction extraction from texts by molecular structure information"):
  with members:
    - "neural message passing for quantum chemistry"
    - "gated graph sequence neural networks"
    - "adam a method for stochastic optimization"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "what matters in a transferable neural network model for relation classification in the biomedical domain"):
  with members:
    - "transfer learning for sequence tagging with hierarchical recurrent networks"
    - "a bi lstm rnn model for relation classification using low cost sequence features"
    - "relation extraction from clinical texts using domain invariant convolutional neural network"
    - "how transferable are neural networks in nlp applications"
    - "transfer learning for low resource neural machine translation"
    - "named entity recognition with bidirectional lstm cnns"
    - "relation classification via recurrent neural network"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "drug drug interaction extraction via recurrent neural network with multiple attention layers"):
  with members:
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "adam a method for stochastic optimization"

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)