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: "neural architectures for open type relation argument extraction"
  Abstract: In this work, we introduce the task of Open-Type Relation Argument Extraction (ORAE): Given a corpus, a query entity Q and a knowledge base relation (e.g.,"Q authored notable work with title X"), the model has to extract an argument of non-standard entity type (entities that cannot be extracted by a standard named entity tagger, e.g. X: the title of a book or a work of art) from the corpus. A distantly supervised dataset based on WikiData relations is obtained and released to address the task. #R##N#We develop and compare a wide range of neural models for this task yielding large improvements over a strong baseline obtained with a neural question answering system. The impact of different sentence encoding architectures and answer extraction methods is systematically compared. An encoder based on gated recurrent units combined with a conditional random fields tagger gives the best results.

Connected through hyperedge HE-1 (induced by source paper "semi supervised few shot learning for dual question answer extraction"):
  with members:
    - "automating reading comprehension by generating question and answer pairs"
    - "qa4ie a question answering based framework for information extraction"
    - "two stage synthesis networks for transfer learning in machine comprehension"
    - "attention is all you need"
    - "a joint model for question answering and question generation"
    - "zero shot relation extraction via reading comprehension"
    - "convolutional sequence to sequence learning"
    - "question answering and question generation as dual tasks"

Connected through hyperedge HE-2 (induced by source paper "qa4ie a question answering based framework for information extraction"):
  with members:
    - "reinforced mnemonic reader for machine comprehension"
    - "memen multi layer embedding with memory networks for machine comprehension"
    - "stochastic answer networks for machine reading comprehension"
    - "joint extraction of entities and relations based on a novel tagging scheme"
    - "zero shot relation extraction via reading comprehension"
    - "reinforced mnemonic reader for machine reading comprehension"
    - "reading wikipedia to answer open domain questions"
    - "wikireading a novel large scale language understanding task over wikipedia"

Connected through hyperedge HE-3 (induced by source paper "building dynamic knowledge graphs from text using machine reading comprehension"):
  with members:
    - "tracking state changes in procedural text a challenge dataset and models for process paragraph comprehension"
    - "reasoning about actions and state changes by injecting commonsense knowledge"
    - "question answering on knowledge bases and text using universal schema and memory networks"
    - "zero shot relation extraction via reading comprehension"
    - "relnet end to end modeling of entities relations"
    - "fasttext zip compressing text classification models"
    - "simulating action dynamics with neural process networks"
    - "graph based convolutional neural network"

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)