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 "can a gorilla ride a camel learning semantic plausibility from text"):
  Abstract: Modeling semantic plausibility requires commonsense knowledge about the world and has been used as a testbed for exploring various knowledge representations. Previous work has focused specifically on modeling physical plausibility and shown that distributional methods fail when tested in a supervised setting. At the same time, distributional models, namely large pretrained language models, have led to improved results for many natural language understanding tasks. In this work, we show that these pretrained language models are in fact effective at modeling physical plausibility in the supervised setting. We therefore present the more difficult problem of learning to model physical plausibility directly from text. We create a training set by extracting attested events from a large corpus, and we provide a baseline for training on these attested events in a self-supervised manner and testing on a physical plausibility task. We believe results could be further improved by injecting explicit commonsense knowledge into a distributional model.

Includes members:
  Member "universal dependency parsing from scratch":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning spatiotemporal features via video and text pair discrimination")
  Member "solving hard coreference problems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "winogrande an adversarial winograd schema challenge at scale")
  Member "sp 10k a large scale evaluation set for selectional preference acquisition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multiplex word embeddings for selectional preference acquisition")
  Member "exploring unsupervised pretraining and sentence structure modelling for winograd schema challenge":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "align mask and select a simple method for incorporating commonsense knowledge into language representation models")
  Member "do neural language representations learn physical commonsense":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ecological semantics programming environments for situated language understanding")
  Member "probing neural network comprehension of natural language arguments":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "reclor a reading comprehension dataset requiring logical reasoning")
  Member "winogrande an adversarial winograd schema challenge at scale":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "natural language qa approaches using reasoning with external knowledge")
  Member "annotation artifacts in natural language inference data":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "adversarial filters of dataset biases")

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)