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: "question answering from unstructured text by retrieval and comprehension"
  Abstract: Open domain Question Answering (QA) systems must interact with external knowledge sources, such as web pages, to find relevant information. Information sources like Wikipedia, however, are not well structured and difficult to utilize in comparison with Knowledge Bases (KBs). In this work we present a two-step approach to question answering from unstructured text, consisting of a retrieval step and a comprehension step. For comprehension, we present an RNN based attention model with a novel mixture mechanism for selecting answers from either retrieved articles or a fixed vocabulary. For retrieval we introduce a hand-crafted model and a neural model for ranking relevant articles. We achieve state-of-the-art performance on W IKI M OVIES dataset, reducing the error by 40%. Our experimental results further demonstrate the importance of each of the introduced components.

Connected through hyperedge HE-1 (induced by source paper "identifying supporting facts for multi hop question answering with document graph networks"):
  with members:
    - "transforming complex sentences into a semantic hierarchy"
    - "graph neural networks a review of methods and applications"
    - "bilinear attention networks"
    - "graphene semantically linked propositions in open information extraction"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "learning to search in long documents using document structure"
    - "know what you don t know unanswerable questions for squad"
    - "relational inductive biases deep learning and graph networks"

Connected through hyperedge HE-2 (induced by source paper "pullnet open domain question answering with iterative retrieval on knowledge bases and text"):
  with members:
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "the web as a knowledge base for answering complex questions"
    - "phase conductor on multi layered attentions for machine comprehension"
    - "open domain question answering using early fusion of knowledge bases and text"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "variational reasoning for question answering with knowledge graph"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"

Connected through hyperedge HE-3 (induced by source paper "learning to search in long documents using document structure"):
  with members:
    - "towards inference oriented reading comprehension parallelqa"
    - "rainbow combining improvements in deep reinforcement learning"
    - "evidence aggregation for answer re ranking in open domain question answering"
    - "contextualized word representations for reading comprehension"
    - "neural speed reading via skim rnn"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "learning recurrent span representations for extractive question answering"
    - "go for a walk and arrive at the answer reasoning over paths in knowledge bases using reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "the web as a knowledge base for answering complex questions"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "from language to programs bridging reinforcement learning and maximum marginal likelihood"
    - "race large scale reading comprehension dataset from examinations"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "searchqa a new q a dataset augmented with context from a search engine"
    - "evaluating semantic parsing against a simple web based question answering model"
    - "ask the right questions active question reformulation with reinforcement learning"
    - "reading wikipedia to answer open domain questions"

Connected through hyperedge HE-5 (induced by source paper "open domain question answering using early fusion of knowledge bases and text"):
  with members:
    - "weaver deep co encoding of questions and documents for machine reading"
    - "the web as a knowledge base for answering complex questions"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "walk steered convolution for graph classification"
    - "evidence aggregation for answer re ranking in open domain question answering"
    - "reinforced mnemonic reader for machine comprehension"
    - "object oriented neural programming oonp for document understanding"
    - "improved neural relation detection for knowledge base question answering"

Connected through hyperedge HE-6 (induced by source paper "clicr a dataset of clinical case reports for machine reading comprehension"):
  with members:
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "quasar datasets for question answering by search and reading"
    - "constructing datasets for multi hop reading comprehension across documents"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "wikireading a novel large scale language understanding task over wikipedia"
    - "text understanding with the attention sum reader network"
    - "squad 100 000 questions for machine comprehension of text"
    - "ms marco a human generated machine reading comprehension dataset"

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)