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: "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.

Connected through hyperedge HE-1 (induced by source paper "sub event detection from twitter streams as a sequence labeling problem"):
  with members:
    - "joint entity recognition and relation extraction as a multi head selection problem"
    - "adversarial training for multi context joint entity and relation extraction"
    - "global normalization of convolutional neural networks for joint entity and relation classification"
    - "sub story detection in twitter with hierarchical dirichlet processes"
    - "convolutional neural networks for sentence classification"
    - "towards real time summarization of scheduled events from twitter streams"
    - "human as real time sensors of social and physical events a case study of twitter and sports games"

Connected through hyperedge HE-2 (induced by source paper "joint entity recognition and relation extraction as a multi head selection problem"):
  with members:
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "dependency parsing as head selection"
    - "neural architectures for named entity recognition"
    - "end to end relation extraction using lstms on sequences and tree structures"
    - "tensorflow a system for large scale machine learning"
    - "combining recurrent and convolutional neural networks for relation classification"
    - "bidirectional lstm crf models for sequence tagging"
    - "classifying relations by ranking with convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "adversarial training for multi context joint entity and relation extraction"):
  with members:
    - "neural machine translation by jointly learning to align and translate"

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)