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: "dataset for the first evaluation on chinese machine reading comprehension"
  Abstract: Machine Reading Comprehension (MRC) has become enormously popular recently and has attracted a lot of attention. However, existing reading comprehension datasets are mostly in English. To add diversity in reading comprehension datasets, in this paper we propose a new Chinese reading comprehension dataset for accelerating related research in the community. The proposed dataset contains two different types: cloze-style reading comprehension and user query reading comprehension, associated with large-scale training data as well as human-annotated validation and hidden test set. Along with this dataset, we also hosted the first Evaluation on Chinese Machine Reading Comprehension (CMRC-2017) and successfully attracted tens of participants, which suggest the potential impact of this dataset.

Connected through hyperedge HE-1 (induced by source paper "investigating prior knowledge for challenging chinese machine reading comprehension"):
  with members:
    - "boolq exploring the surprising difficulty of natural yes no questions"
    - "ernie enhanced representation through knowledge integration"
    - "geosqa a benchmark for scenario based question answering in the geography domain at high school level"
    - "pre training with whole word masking for chinese bert"
    - "dream a challenge dataset and models for dialogue based reading comprehension"
    - "chid a large scale chinese idiom dataset for cloze test"
    - "analogical reasoning on chinese morphological and semantic relations"
    - "exploiting sentence embedding for medical question answering"

Connected through hyperedge HE-2 (induced by source paper "effective subword segmentation for text comprehension"):
  with members:
    - "dependency or span end to end uniform semantic role labeling"
    - "discourse marker augmented network with reinforcement learning for natural language inference"
    - "dual co matching network for multi choice reading comprehension"
    - "i know what you want semantic learning for text comprehension"
    - "graph based bilingual word embedding for statistical machine translation"
    - "open vocabulary learning for neural chinese pinyin ime"
    - "deep contextualized word representations"
    - "subword augmented embedding for cloze reading comprehension"

Connected through hyperedge HE-3 (induced by source paper "effective character augmented word embedding for machine reading comprehension"):
  with members:
    - "deep enhanced representation for implicit discourse relation recognition"
    - "deep contextualized word representations"
    - "subword augmented embedding for cloze reading comprehension"
    - "one shot learning for question answering in gaokao history challenge"
    - "multi passage machine reading comprehension with cross passage answer verification"
    - "modeling multi turn conversation with deep utterance aggregation"
    - "lingke a fine grained multi turn chatbot for customer service"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"

Connected through hyperedge HE-4 (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"
    - "reinforced mnemonic reader for machine reading comprehension"
    - "dureader a chinese machine reading comprehension dataset from real world applications"
    - "generating and exploiting large scale pseudo training data for zero pronoun resolution"

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)