human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "unified neural architecture for drug disease and clinical entity recognition"):
  Abstract: Abstract   Most existing methods for biomedical entity recognition tasks rely on explicit feature engineering where many features are either specific to a particular task or depend on the output of other natural language processing tools. Neural architectures have shown across various domains that efforts for explicit feature design can be reduced. In this work, we propose a unified framework using a bi-directional long short-term memory network (BLSTM) for named entity recognition (NER) tasks in biomedical and clinical domains. Three important characteristics of the framework are as follows: (1) The model learns contextual as well as morphological features using two different BLSTMs in a hierarchy, (2) the model uses a first-order linear conditional random field (CRF) in its output layer in cascade of BLSTM to infer label or tag sequence, and (3) the model does not use any domain-specific features or dictionary, that is, in another words, the same set of features are used in the three NER tasks, namely, disease name recognition (Disease NER), drug name recognition (Drug NER), and clinical entity recognition (Clinical NER). We compare the performance of the proposed model with existing state-of-the-art models on the standard benchmark datasets of the three tasks. We show empirically that the proposed framework outperforms all existing models. We analyze the importance of CRF layer, different feature types, and word embedding obtained using character-based embedding. The error analysis of the model indicates that a major proportion of errors are due to difficulty in recognizing acronyms and nested forms of entity names.

Includes members:
  Member "recurrent neural network models for disease name recognition using domain invariant features":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "collabonet collaboration of deep neural networks for biomedical named entity recognition")
  Member "neural architectures for named entity recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "knowledge graphs")
  Member "classifying relations by ranking with convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "generating sequences with recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "advances in optimizing recurrent networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "heterogeneous multilayer generalized operational perceptron")
  Member "natural language processing almost from scratch":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")

Question: Please tell me which class the source paper 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)