human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "solving hard coreference problems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "winogrande an adversarial winograd schema challenge at scale")
  Member "mind the gap a balanced corpus of gendered ambiguous pronouns":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a framework for evaluation of machine reading comprehension gold standards")
  Member "gender bias in coreference resolution":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "algorithmic fairness")
  Member "a simple method for commonsense reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "minilm deep self attention distillation for task agnostic compression of pre trained transformers")
  Member "on the evaluation of common sense reasoning in natural language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "align mask and select a simple method for incorporating commonsense knowledge into language representation models")
  Member "higher order coreference resolution with coarse to fine inference":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multiplex word embeddings for selectional preference acquisition")
  Member "bert pre training of deep bidirectional transformers for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "gender bias in coreference resolution evaluation and debiasing methods":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a framework for evaluation of machine reading comprehension gold standards")

Question: Please tell me which class the source paper 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)