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: "emergent logical structure in vector representations of neural readers"
  Abstract: Reading comprehension is a question answering task where the answer is to be found in a given passage about entities and events not mentioned in general knowledge sources. A significant number of neural architectures for this task (neural readers) have recently been developed and evaluated on large cloze-style datasets. We present experiments supporting the existence of logical structure in the hidden state vectors of "aggregation readers" such as the Attentive Reader and Stanford Reader. The logical structure of aggregation readers reflects the architecture of "explicit reference readers" such as the Attention-Sum Reader, the Gated Attention Reader and the Attention-over-Attention Reader. This relationship between aggregation readers and explicit reference readers presents a case study in emergent logical structure. In an independent contribution, we show that the addition of linguistics features to the input to existing neural readers significantly boosts performance yielding the best results to date on the Who-did-What datasets.

Connected through hyperedge HE-1 (induced by source paper "neural models for reasoning over multiple mentions using coreference"):
  with members:
    - "cross sentence n ary relation extraction with graph lstms"
    - "end to end neural coreference resolution"
    - "syntax aware lstm model for chinese semantic role labeling"
    - "attention is all you need"
    - "frustratingly short attention spans in neural language modeling"
    - "improved neural machine translation with a syntax aware encoder and decoder"
    - "linguistic knowledge as memory for recurrent neural networks"
    - "dynamic entity representations in neural language models"

Connected through hyperedge HE-2 (induced by source paper "linguistic knowledge as memory for recurrent neural networks"):
  with members:
    - "frustratingly short attention spans in neural language modeling"
    - "a neural knowledge language model"
    - "a thorough examination of the cnn daily mail reading comprehension task"
    - "broad context language modeling as reading comprehension"
    - "reasoning with memory augmented neural networks for language comprehension"
    - "gated graph sequence neural networks"
    - "text understanding with the attention sum reader network"
    - "squad 100 000 questions for machine comprehension of text"

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)