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 "learning to infer entities properties and their relations from clinical conversations"):
  Abstract: Recently we proposed the Span Attribute Tagging (SAT) Model (Du et al., 2019) to infer clinical entities (e.g., symptoms) and their properties (e.g., duration). It tackles the challenge of large label space and limited training data using a hierarchical two-stage approach that identifies the span of interest in a tagging step and assigns labels to the span in a classification step. #R##N#We extend the SAT model to jointly infer not only entities and their properties but also relations between them. Most relation extraction models restrict inferring relations between tokens within a few neighboring sentences, mainly to avoid high computational complexity. In contrast, our proposed Relation-SAT (R-SAT) model is computationally efficient and can infer relations over the entire conversation, spanning an average duration of 10 minutes. #R##N#We evaluate our model on a corpus of clinical conversations. When the entities are given, the R-SAT outperforms baselines in identifying relations between symptoms and their properties by about 32% (0.82 vs 0.62 F-score) and by about 50% (0.60 vs 0.41 F-score) on medications and their properties. On the more difficult task of jointly inferring entities and relations, the R-SAT model achieves a performance of 0.34 and 0.45 for symptoms and medications respectively, which is significantly better than 0.18 and 0.35 for the baseline model. The contributions of different components of the model are quantified using ablation analysis.

Includes members:
  Member "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning from explanations with neural execution tree")
  Member "extracting symptoms and their status from clinical conversations":
    also connected through other hyperedges:
      (none)
  Member "simultaneously self attending to all mentions for full abstract biological relation extraction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "leveraging dependency forest for neural medical relation extraction")
  Member "neural relation extraction within and across sentence boundaries":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "towards understanding gender bias in relation extraction")
  Member "graph convolution over pruned dependency trees improves relation extraction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "k adapter infusing knowledge into pre trained models with adapters")
  Member "multi labeled relation extraction with attentive capsule network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discovering protagonist of sentiment with aspect reconstructed capsule network")
  Member "multi task learning for sequence tagging an empirical study":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "word embedding algorithms as generalized low rank models and their canonical form")
  Member "a hierarchical framework for relation extraction with reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "entity relation extraction as multi turn question answering")

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)