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 "combining spans into entities a neural two stage approach for recognizing discontiguous entities"):
  Abstract: In medical documents, it is possible that an entity of interest not only contains a discontiguous sequence of words but also overlaps with another entity. Entities of such structures are intrinsically hard to recognize due to the large space of possible entity combinations. In this work, we propose a neural two-stage approach to recognize discontiguous and overlapping entities by decomposing this problem into two subtasks: 1) it first detects all the overlapping spans that either form entities on their own or present as segments of discontiguous entities, based on the representation of segmental hypergraph, 2) next it learns to combine these segments into discontiguous entities with a classifier, which filters out other incorrect combinations of segments. Two neural components are designed for these subtasks respectively and they are learned jointly using a shared encoder for text. Our model achieves the state-of-the-art performance in a standard dataset, even in the absence of external features that previous methods used.

Includes members:
  Member "sequence to nuggets nested entity mention detection via anchor region networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a unified mrc framework for named entity recognition")
  Member "merge and label a novel neural network architecture for nested ner":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a unified mrc framework for named entity recognition")
  Member "neural architectures for nested ner through linearization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "detecting potential topics in news using bert crf and wikipedia")
  Member "neural segmental hypergraphs for overlapping mention recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a unified mrc framework for named entity recognition")
  Member "a neural transition based model for nested mention recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "query based named entity recognition")
  Member "labeling gaps between words recognizing overlapping mentions with mention separators":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi grained named entity recognition")
  Member "heterogeneous supervision for relation extraction a representation learning approach":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "keml a knowledge enriched meta learning framework for lexical relation classification")
  Member "sequence to sequence learning as beam search optimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "gradient based adversarial attacks on categorical sequence models via traversing an embedded world")

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)