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: "brundlefly at semeval 2016 task 12 recurrent neural networks vs joint inference for clinical temporal information extraction"
  Abstract: We submitted two systems to the SemEval-2016 Task 12: Clinical TempEval challenge, participating in Phase 1, where we identified text spans of time and event expressions in clinical notes and Phase 2, where we predicted a relation between an event and its parent document creation time. #R##N#For temporal entity extraction, we find that a joint inference-based approach using structured prediction outperforms a vanilla recurrent neural network that incorporates word embeddings trained on a variety of large clinical document sets. For document creation time relations, we find that a combination of date canonicalization and distant supervision rules for predicting relations on both events and time expressions improves classification, though gains are limited, likely due to the small scale of training data.

Connected through hyperedge HE-1 (induced by source paper "medical device surveillance with electronic health records"):
  with members:
    - "snorkel rapid training data creation with weak supervision"
    - "bag of tricks for efficient text classification"

Connected through hyperedge HE-2 (induced by source paper "raim recurrent attentive and intensive model of multimodal patient monitoring data"):
  with members:
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "cardiologist level arrhythmia detection with convolutional neural networks"
    - "beat by beat classifying cardiac arrhythmias with recurrent neural networks"
    - "attention is all you need"
    - "highrisk prediction from electronic medical records via deep attention networks"
    - "deep learning to attend to risk in icu"
    - "dipole diagnosis prediction in healthcare via attention based bidirectional recurrent neural networks"
    - "temporal relational reasoning in videos"

Connected through hyperedge HE-3 (induced by source paper "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"):
  with members:
    - "a short review of ethical challenges in clinical natural language processing"
    - "medical concept representation learning from electronic health records and its application on heart failure prediction"
    - "bidirectional recurrent neural networks for medical event detection in electronic health records"
    - "de identification of patient notes with recurrent neural networks"
    - "structured prediction models for rnn based sequence labeling in clinical text"
    - "recurrent neural networks for multivariate time series with missing values"
    - "doctor ai predicting clinical events via recurrent neural networks"
    - "incremental knowledge base construction using deepdive"

Connected through hyperedge HE-4 (induced by source paper "de identification of patient notes with recurrent neural networks"):
  with members:
    - "neural architectures for named entity recognition"
    - "sequential short text classification with recurrent and convolutional neural networks"
    - "character aware neural language models"
    - "towards ai complete question answering a set of prerequisite toy tasks"
    - "convolutional neural networks for sentence classification"
    - "neural machine translation by jointly learning to align and translate"
    - "a convolutional neural network for modelling sentences"
    - "mantime temporal expression identification and normalization in the tempeval 3 challenge"

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)