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: "generating and exploiting large scale pseudo training data for zero pronoun resolution"
  Abstract: Most existing approaches for zero pronoun resolution are heavily relying on annotated data, which is often released by shared task organizers. Therefore, the lack of annotated data becomes a major obstacle in the progress of zero pronoun resolution task. Also, it is expensive to spend manpower on labeling the data for better performance. To alleviate the problem above, in this paper, we propose a simple but novel approach to automatically generate large-scale pseudo training data for zero pronoun resolution. Furthermore, we successfully transfer the cloze-style reading comprehension neural network model into zero pronoun resolution task and propose a two-step training mechanism to overcome the gap between the pseudo training data and the real one. Experimental results show that the proposed approach significantly outperforms the state-of-the-art systems with an absolute improvements of 3.1% F-score on OntoNotes 5.0 data.

Connected through hyperedge HE-1 (induced by source paper "cross lingual machine reading comprehension"):
  with members:
    - "data augmentation for bert fine tuning in open domain question answering"
    - "towards robust neural machine translation"
    - "multilingual extractive reading comprehension by runtime machine translation"
    - "drcd a chinese machine reading comprehension dataset"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "read verify machine reading comprehension with unanswerable questions"
    - "a span extraction dataset for chinese machine reading comprehension"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "discriminative sentence modeling for story ending prediction"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "self normalizing neural networks"
    - "attention over attention neural networks for reading comprehension"
    - "the effect of different writing tasks on linguistic style a case study of the roc story cloze task"
    - "bidirectional attention flow for machine comprehension"
    - "text understanding with the attention sum reader network"
    - "squad 100 000 questions for machine comprehension of text"
    - "consensus attention based neural networks for chinese reading comprehension"

Connected through hyperedge HE-3 (induced by source paper "a span extraction dataset for chinese machine reading comprehension"):
  with members:
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "drcd a chinese machine reading comprehension dataset"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "race large scale reading comprehension dataset from examinations"
    - "dataset for the first evaluation on chinese machine reading comprehension"
    - "reinforced mnemonic reader for machine reading comprehension"
    - "dureader a chinese machine reading comprehension dataset from real world applications"

Connected through hyperedge HE-4 (induced by source paper "deep reinforcement learning for chinese zero pronoun resolution"):
  with members:
    - "deeppath a reinforcement learning method for knowledge graph reasoning"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "deep reinforcement learning for dialogue generation"
    - "a deep neural network for chinese zero pronoun resolution"
    - "deep reinforcement learning for mention ranking coreference models"
    - "improving information extraction by acquiring external evidence with reinforcement learning"
    - "learning to win by reading manuals in a monte carlo framework"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "attention over attention neural networks for reading comprehension"):
  with members:
    - "a thorough examination of the cnn daily mail reading comprehension task"
    - "bidirectional attention flow for machine comprehension"
    - "text understanding with the attention sum reader network"
    - "iterative alternating neural attention for machine reading"
    - "consensus attention based neural networks for chinese reading comprehension"
    - "dynamic coattention networks for question answering"
    - "theano a python framework for fast computation of mathematical expressions"
    - "natural language comprehension with the epireader"

Connected through hyperedge HE-6 (induced by source paper "the first evaluation of chinese human computer dialogue technology"):
  with members:
    - "attention over attention neural networks for reading comprehension"
    - "text understanding with the attention sum reader network"
    - "consensus attention based neural networks for chinese reading comprehension"
    - "topic augmented neural response generation with a joint attention mechanism"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "neural responding machine for short text conversation"
    - "stochastic language generation in dialogue using recurrent neural networks with convolutional sentence reranking"

Connected through hyperedge HE-7 (induced by source paper "dataset for the first evaluation on chinese machine reading comprehension"):
  with members:
    - "attention over attention neural networks for reading comprehension"
    - "a thorough examination of the cnn daily mail reading comprehension task"
    - "text understanding with the attention sum reader network"
    - "squad 100 000 questions for machine comprehension of text"
    - "consensus attention based neural networks for chinese reading comprehension"
    - "the goldilocks principle reading children s books with explicit memory representations"
    - "teaching machines to read and comprehend"

Connected through hyperedge HE-8 (induced by source paper "consensus attention based neural networks for chinese reading comprehension"):
  with members:
    - "a thorough examination of the cnn daily mail reading comprehension task"
    - "text understanding with the attention sum reader network"
    - "theano a python framework for fast computation of mathematical expressions"
    - "the goldilocks principle reading children s books with explicit memory representations"
    - "teaching machines to read and comprehend"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"
    - "exact solutions to the nonlinear dynamics of learning in deep linear neural networks"

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)