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: "are distributional representations ready for the real world evaluating word vectors for grounded perceptual meaning"
  Abstract: Distributional word representation methods exploit word co-occurrences to build compact vector encodings of words. While these representations enjoy widespread use in modern natural language processing, it is unclear whether they accurately encode all necessary facets of conceptual meaning. In this paper, we evaluate how well these representations can predict perceptual and conceptual features of concrete concepts, drawing on two semantic norm datasets sourced from human participants. We find that several standard word representations fail to encode many salient perceptual features of concepts, and show that these deficits correlate with word-word similarity prediction errors. Our analyses provide motivation for grounded and embodied language learning approaches, which may help to remedy these deficits.

Connected through hyperedge HE-1 (induced by source paper "can a gorilla ride a camel learning semantic plausibility from text"):
  with members:
    - "universal dependency parsing from scratch"
    - "solving hard coreference problems"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "socialiqa commonsense reasoning about social interactions"):
  with members:
    - "a surprisingly robust trick for the winograd schema challenge"
    - "solving hard coreference problems"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "cracking the contextual commonsense code understanding commonsense reasoning aptitude of deep contextual representations"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "scibert pretrained contextualized embeddings for scientific text"
    - "ernie enhanced language representation with informative entities"
    - "do neural language representations learn physical commonsense"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "linguistic knowledge and transferability of contextual representations"
    - "winogrande an adversarial winograd schema challenge at scale"
    - "roberta a robustly optimized bert pretraining approach"

Connected through hyperedge HE-4 (induced by source paper "feature2vec distributional semantic modelling of human property knowledge"):
  with members:
    - "semantic structure and interpretability of word embeddings"
    - "using sparse semantic embeddings learned from multimodal text and image data to model human conceptual knowledge"
    - "firearms and tigers are dangerous kitchen knives and zebras are not testing whether word embeddings can tell"
    - "enriching word vectors with subword information"
    - "word2vec explained deriving mikolov et al s negative sampling word embedding method"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "do neural language representations learn physical commonsense"):
  with members:
    - "human few shot learning of compositional instructions"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "playing by the book an interactive game approach for action graph extraction from text"):
  with members:
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "question answering as global reasoning over semantic abstractions"
    - "a survey of domain adaptation for neural machine translation"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "textworld a learning environment for text based games"
    - "building dynamic knowledge graphs from text using machine reading comprehension"
    - "deep dyna q integrating planning for task completion dialogue policy learning"
    - "learn what not to learn action elimination with deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "using sparse semantic embeddings learned from multimodal text and image data to model human conceptual knowledge"):
  with members:
    - "semantic structure and interpretability of word embeddings"
    - "vqa visual question answering"
    - "sparse overcomplete word vector representations"
    - "combining language and vision with a multimodal skip gram model"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "visualizing and understanding convolutional networks"
    - "distributed representations of words and phrases and their compositionality"
    - "non negative sparse coding"

Connected through hyperedge HE-8 (induced by source paper "simulating action dynamics with neural process networks"):
  with members:
    - "dynamic entity representations in neural language models"
    - "hadamard product for low rank bilinear pooling"
    - "tracking the world state with recurrent entity networks"
    - "key value memory networks for directly reading documents"
    - "learning a natural language interface with neural programmer"
    - "modeling coverage for neural machine translation"
    - "reference aware language models"
    - "query reduction networks for question answering"

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)