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: "end to end answer chunk extraction and ranking for reading comprehension"
  Abstract: This paper proposes dynamic chunk reader (DCR), an end-to-end neural reading comprehension (RC) model that is able to extract and rank a set of answer candidates from a given document to answer questions. DCR is able to predict answers of variable lengths, whereas previous neural RC models primarily focused on predicting single tokens or entities. DCR encodes a document and an input question with recurrent neural networks, and then applies a word-by-word attention mechanism to acquire question-aware representations for the document, followed by the generation of chunk representations and a ranking module to propose the top-ranked chunk as the answer. Experimental results show that DCR could achieve a 66.3% Exact match and 74.7% F1 score on the Stanford Question Answering Dataset.

Connected through hyperedge HE-1 (induced by source paper "curriculum learning strategies for ir an empirical study on conversation response ranking"):
  with members:
    - "bert with history answer embedding for conversational question answering"
    - "passage re ranking with bert"
    - "introducing mantis a novel multi domain information seeking dialogues dataset"
    - "faq retrieval using query question similarity and bert based query answer relevance"
    - "on the power of curriculum learning in training deep networks"
    - "image difficulty curriculum for generative adversarial networks cugan"
    - "simple applications of bert for ad hoc document retrieval"
    - "a hybrid retrieval generation neural conversation model"

Connected through hyperedge HE-2 (induced by source paper "improving background based conversation with context aware knowledge pre selection"):
  with members:
    - "improving neural response diversity with frequency aware cross entropy loss"
    - "learning to select knowledge for response generation in dialog systems"
    - "wizard of wikipedia knowledge powered conversational agents"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "generating informative and diverse conversational responses via adversarial information maximization"
    - "towards exploiting background knowledge for building conversation systems"
    - "learning recurrent span representations for extractive question answering"
    - "bidirectional attention flow for machine comprehension"

Connected through hyperedge HE-3 (induced by source paper "a fully attention based information retriever"):
  with members:
    - "phase conductor on multi layered attentions for machine comprehension"
    - "learning to compute word embeddings on the fly"
    - "contextualized word representations for reading comprehension"
    - "reinforced mnemonic reader for machine comprehension"
    - "smarnet teaching machines to read and comprehend like human"
    - "attention is all you need"
    - "structural embedding of syntactic trees for machine comprehension"
    - "stochastic answer networks for machine reading comprehension"

Connected through hyperedge HE-4 (induced by source paper "a question focused multi factor attention network for question answering"):
  with members:
    - "dynamic integration of background knowledge in neural nlu systems"
    - "memen multi layer embedding with memory networks for machine comprehension"
    - "two stage synthesis networks for transfer learning in machine comprehension"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "searchqa a new q a dataset augmented with context from a search engine"
    - "attention over attention neural networks for reading comprehension"
    - "learning recurrent span representations for extractive question answering"
    - "making neural qa as simple as possible but not simpler"

Connected through hyperedge HE-5 (induced by source paper "caesar context awareness enabled summary attentive reader"):
  with members:
    - "learning recurrent span representations for extractive question answering"
    - "reasonet learning to stop reading in machine comprehension"
    - "bidirectional attention flow for machine comprehension"
    - "attention based convolutional neural network for machine comprehension"
    - "squad 100 000 questions for machine comprehension of text"
    - "learning a natural language interface with neural programmer"
    - "dynamic coattention networks for question answering"
    - "multi perspective context matching for machine comprehension"

Connected through hyperedge HE-6 (induced by source paper "a unified query based generative model for question generation and question answering"):
  with members:
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "neural models for key phrase detection and question generation"
    - "learning to ask neural question generation for reading comprehension"
    - "a joint model for question answering and question generation"
    - "learning recurrent span representations for extractive question answering"
    - "neural question generation from text a preliminary study"
    - "get to the point summarization with pointer generator networks"
    - "reasonet learning to stop reading in machine comprehension"

Connected through hyperedge HE-7 (induced by source paper "a comparative study of word embeddings for reading comprehension"):
  with members:
    - "attention over attention neural networks for reading comprehension"
    - "reasonet learning to stop reading in machine comprehension"
    - "newsqa a machine comprehension dataset"
    - "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"
    - "squad 100 000 questions for machine comprehension of text"
    - "iterative alternating neural attention for machine reading"

Connected through hyperedge HE-8 (induced by source paper "r 3 reinforced reader ranker for open domain question answering"):
  with members:
    - "improved neural relation detection for knowledge base question answering"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "searchqa a new q a dataset augmented with context from a search engine"
    - "quasar datasets for question answering by search and reading"
    - "reading wikipedia to answer open domain questions"
    - "enhanced lstm for natural language inference"
    - "squad 100 000 questions for machine comprehension of text"
    - "neural summarization by extracting sentences and words"

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)