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: "solving hard coreference problems"
  Abstract: Coreference resolution is a key problem in natural language understanding that still escapes reliable solutions. One fundamental difficulty has been that of resolving instances involving pronouns since they often require deep language understanding and use of background knowledge. In this paper, we propose an algorithmic solution that involves a new representation for the knowledge required to address hard coreference problems, along with a constrained optimization framework that uses this knowledge in coreference decision making. Our representation, Predicate Schemas, is instantiated with knowledge acquired in an unsupervised way, and is compiled automatically into constraints that impact the coreference decision. We present a general coreference resolution system that significantly improves state-of-the-art performance on hard, Winograd-style, pronoun resolution cases, while still performing at the state-of-the-art level on standard coreference resolution datasets.

Connected through hyperedge HE-1 (induced by source paper "winogrande an adversarial winograd schema challenge at scale"):
  with members:
    - "a surprisingly robust trick for the winograd schema challenge"
    - "are we modeling the task or the annotator an investigation of annotator bias in natural language understanding datasets"
    - "a hybrid neural network model for commonsense reasoning"
    - "probing neural network comprehension of natural language arguments"
    - "superglue a stickier benchmark for general purpose language understanding systems"
    - "roberta a robustly optimized bert pretraining approach"
    - "on adversarial removal of hypothesis only bias in natural language inference"
    - "multinomial adversarial networks for multi domain text classification"

Connected through hyperedge HE-2 (induced by source paper "can a gorilla ride a camel learning semantic plausibility from text"):
  with members:
    - "universal dependency parsing from scratch"
    - "sp 10k a large scale evaluation set for selectional preference acquisition"
    - "exploring unsupervised pretraining and sentence structure modelling for winograd schema challenge"
    - "do neural language representations learn physical commonsense"
    - "probing neural network comprehension of natural language arguments"
    - "winogrande an adversarial winograd schema challenge at scale"
    - "annotation artifacts in natural language inference data"
    - "hypothesis only baselines in natural language inference"

Connected through hyperedge HE-3 (induced by source paper "socialiqa commonsense reasoning about social interactions"):
  with members:
    - "a surprisingly robust trick for the winograd schema challenge"
    - "winogrande an adversarial winograd schema challenge at scale"
    - "from recognition to cognition visual commonsense reasoning"
    - "sentence encoders on stilts supplementary training on intermediate labeled data tasks"
    - "annotation artifacts in natural language inference data"
    - "evaluating theory of mind in question answering"
    - "machine common sense concept paper"
    - "know what you don t know unanswerable questions for squad"

Connected through hyperedge HE-4 (induced by source paper "gender bias in contextualized word embeddings"):
  with members:
    - "on measuring social biases in sentence encoders"
    - "equalizing gender biases in neural machine translation with word embeddings techniques"
    - "lipstick on a pig debiasing methods cover up systematic gender biases in word embeddings but do not remove them"
    - "gender bias in coreference resolution"
    - "getting gender right in neural machine translation"
    - "deep contextualized word representations"
    - "higher order coreference resolution with coarse to fine inference"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-5 (induced by source paper "exploring unsupervised pretraining and sentence structure modelling for winograd schema challenge"):
  with members:
    - "universal transformers"
    - "how reasonable are common sense reasoning tasks a case study on the winograd schema challenge and swag"
    - "a simple method for commonsense reasoning"
    - "on the evaluation of common sense reasoning in natural language understanding"
    - "a knowledge hunting framework for common sense reasoning"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-6 (induced by source paper "unsupervised deep structured semantic models for commonsense reasoning"):
  with members:
    - "modeling naive psychology of characters in simple commonsense stories"
    - "a simple method for commonsense reasoning"
    - "deep contextualized word representations"
    - "neural approaches to conversational ai"
    - "event2mind commonsense inference on events intents and reactions"
    - "record bridging the gap between human and machine commonsense reading comprehension"
    - "swag a large scale adversarial dataset for grounded commonsense inference"
    - "a broad coverage challenge corpus for sentence understanding through inference"

Connected through hyperedge HE-7 (induced by source paper "attention is not all you need for commonsense reasoning"):
  with members:
    - "a surprisingly robust trick for the winograd schema challenge"
    - "how reasonable are common sense reasoning tasks a case study on the winograd schema challenge and swag"
    - "a simple method for commonsense reasoning"
    - "on the evaluation of common sense reasoning in natural language understanding"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a simple machine learning method for commonsense reasoning a short commentary on trinh le 2018"

Connected through hyperedge HE-8 (induced by source paper "wikicrem a large unsupervised corpus for coreference resolution"):
  with members:
    - "a surprisingly robust trick for the winograd schema challenge"
    - "the referential reader a recurrent entity network for anaphora resolution"
    - "mind the gap a balanced corpus of gendered ambiguous pronouns"
    - "gender bias in coreference resolution"
    - "a simple method for commonsense reasoning"
    - "a knowledge hunting framework for common sense reasoning"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "bert pre training of deep bidirectional transformers for language understanding"

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)