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 "an attentive neural architecture for joint segmentation and parsing and its application to real estate ads"):
  Abstract: In this paper we develop a relatively simple and effective neural joint model that performs both segmentation and dependency parsing. While the model arose in the context of a particular application, we believe the general idea could be translated to other settings where both (1) entities have to be identified from text, and (2) dependency relationships between them. The application we focus on here is the recently introduced problem of extracting the structured description, which we name property tree, of a real estate property based on the textual advertisement: convert an ad to a tree-structure indicating the buildings, floors, rooms, etc. and how one is part of another. Previous work on the problem focused on a hand-crafted feature-based pipeline approach comprising two different modules, solving the two subtasks of the structured prediction problem: (1) identify important entities of a property (e.g., rooms) from classifieds and (2) structure them into a tree format. In this work, we propose a new joint model that is able to tackle the two tasks simultaneously and construct the property tree by (i) avoiding the error propagation and (ii) exploiting the interactions between the subtasks. For this purpose, we perform an extensive comparative study of the pipeline methods and the new proposed joint model, reporting an improvement of over three percentage points in the overall edge $F_1$ score of the property tree. Also, we have considered several attention methods, to encourage our model to focus on salient tokens during the construction of the property tree. Thus we experimentally demonstrate the usefulness of attentive neural architectures for the proposed joint model, showcasing a further improvement of two percentage points in edge $F_1$ score for our application.

Includes members:
  Member "stochastic configuration networks fundamentals and algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep randomized neural networks")
  Member "neural message passing for quantum chemistry":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "deep biaffine attention for neural dependency parsing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "lost in embedding space explaining cross lingual task performance with eigenvalue divergence")
  Member "end to end sequence labeling via bi directional lstm cnns crf":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "secnlp a survey of embeddings in clinical natural language processing")
  Member "globally normalized transition based neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "consistency of a recurrent language model with respect to incomplete decoding")
  Member "dependency parsing as head selection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "syntactically look ahead attention network for sentence compression")
  Member "simple and accurate dependency parsing using bidirectional lstm feature representations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "parsing as pretraining")
  Member "neural architectures for named entity recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "knowledge graphs")

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)