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 contextualized document representations for healthcare answer retrieval"):
  Abstract: We present Contextual Discourse Vectors (CDV), a distributed document representation for efficient answer retrieval from long healthcare documents. Our approach is based on structured query tuples of entities and aspects from free text and medical taxonomies. Our model leverages a dual encoder architecture with hierarchical LSTM layers and multi-task training to encode the position of clinical entities and aspects alongside the document discourse. We use our continuous representations to resolve queries with short latency using approximate nearest neighbor search on sentence level. We apply the CDV model for retrieving coherent answer passages from nine English public health resources from the Web, addressing both patients and medical professionals. Because there is no end-to-end training data available for all application scenarios, we train our model with self-supervised data from Wikipedia. We show that our generalized model significantly outperforms several state-of-the-art baselines for healthcare passage ranking and is able to adapt to heterogeneous domains without additional fine-tuning.

Includes members:
  Member "biobert a pre trained biomedical language representation model for biomedical text mining":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the microsoft toolkit of multi task deep neural networks for natural language understanding")
  Member "learning dense representations for entity retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "zero shot entity linking with dense entity retrieval")
  Member "a question entailment approach to question answering":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generating scientific question answering corpora from q a forums")
  Member "pubmedqa a dataset for biomedical research question answering":
    also connected through other hyperedges:
      (none)
  Member "zero shot entity linking by reading entity descriptions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "overview of the ccks 2019 knowledge graph evaluation track entity relation event and qa")
  Member "sector a neural model for coherent topic segmentation and classification":
    also connected through other hyperedges:
      (none)
  Member "matchzoo a learning practicing and developing system for neural text matching":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "novel entity discovery from web tables")
  Member "anmm ranking short answer texts with attention based neural matching model":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "iart intent aware response ranking with transformers in information seeking conversation systems")

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)