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: "the hard core coreference corpus removing gender and number cues for difficult pronominal anaphora resolution"
  Abstract: We introduce a new benchmark for coreference resolution and NLI, Knowref, that targets common-sense understanding and world knowledge. Previous coreference resolution tasks can largely be solved by exploiting the number and gender of the antecedents, or have been handcrafted and do not reflect the diversity of naturally occurring text. We present a corpus of over 8,000 annotated text passages with ambiguous pronominal anaphora. These instances are both challenging and realistic. We show that various coreference systems, whether rule-based, feature-rich, or neural, perform significantly worse on the task than humans, who display high inter-annotator agreement. To explain this performance gap, we show empirically that state-of-the art models often fail to capture context, instead relying on the gender or number of candidate antecedents to make a decision. We then use problem-specific insights to propose a data-augmentation trick called antecedent switching to alleviate this tendency in models. Finally, we show that antecedent switching yields promising results on other tasks as well: we use it to achieve state-of-the-art results on the GAP coreference task.

Connected through hyperedge HE-1 (induced by source paper "coreference resolution as query based span prediction"):
  with members:
    - "a unified mrc framework for named entity recognition"
    - "quoref a reading comprehension dataset with questions requiring coreferential reasoning"
    - "ellipsis and coreference resolution as question answering"
    - "spanbert improving pre training by representing and predicting spans"
    - "entity relation extraction as multi turn question answering"
    - "bert for coreference resolution baselines and analysis"
    - "gender bias in contextualized word embeddings"
    - "multiqa an empirical investigation of generalization and transfer in reading comprehension"

Connected through hyperedge HE-2 (induced by source paper "knowledge aware pronoun coreference resolution"):
  with members:
    - "incorporating context and external knowledge for pronoun coreference resolution"
    - "aser a large scale eventuality knowledge graph"
    - "deep contextualized word representations"
    - "higher order coreference resolution with coarse to fine inference"
    - "end to end neural coreference resolution"
    - "deep reinforcement learning for mention ranking coreference models"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-3 (induced by source paper "incorporating context and external knowledge for pronoun coreference resolution"):
  with members:
    - "aser a large scale eventuality knowledge graph"
    - "deep contextualized word representations"
    - "higher order coreference resolution with coarse to fine inference"
    - "end to end neural coreference resolution"
    - "deep reinforcement learning for mention ranking coreference models"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

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)