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: "a method for modeling co occurrence propensity of clinical codes with application to icd 10 pcs auto coding"
  Abstract: Objective. Natural language processing methods for medical auto-coding, or automatic generation of medical billing codes from electronic health records, generally assign each code independently of the others. They may thus assign codes for closely related procedures or diagnoses to the same document, even when they do not tend to occur together in practice, simply because the right choice can be difficult to infer from the clinical narrative. #R##N#Materials and Methods. We propose a method that injects awareness of the propensities for code co-occurrence into this process. First, a model is trained to estimate the conditional probability that one code is assigned by a human coder, given than another code is known to have been assigned to the same document. Then, at runtime, an iterative algorithm is used to apply this model to the output of an existing statistical auto-coder to modify the confidence scores of the codes. #R##N#Results. We tested this method in combination with a primary auto-coder for ICD-10 procedure codes, achieving a 12% relative improvement in F-score over the primary auto-coder baseline. #R##N#Discussion. The proposed method can be used, with appropriate features, in combination with any auto-coder that generates codes with different levels of confidence. #R##N#Conclusion. The promising results obtained for ICD-10 procedure codes suggest that the proposed method may have wider applications in auto-coding.

Connected through hyperedge HE-1 (induced by source paper "automatic documentation of icd codes with far field speech recognition"):
  with members:
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "conditional end to end audio transforms"
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"
    - "an empirical evaluation of deep learning for icd 9 code assignment using mimic iii clinical notes"
    - "speech recognition for medical conversations"
    - "towards better decoding and language model integration in sequence to sequence models"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "multi scale context aggregation by dilated convolutions"

Connected through hyperedge HE-2 (induced by source paper "deeptag inferring all cause diagnoses from clinical notes in under resourced medical domain"):
  with members:
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "comparing rule based and deep learning models for patient phenotyping"
    - "multi label classification of patient notes a case study on icd code assignment"
    - "wavenet a generative model for raw audio"
    - "learning to diagnose with lstm recurrent neural networks"
    - "adam a method for stochastic optimization"
    - "clustered multi task learning a convex formulation"

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)