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 "visually grounded neural syntax acquisition"):
  Abstract: We present the Visually Grounded Neural Syntax Learner (VG-NSL), an approach for learning syntactic representations and structures without any explicit supervision. The model learns by looking at natural images and reading paired captions. VG-NSL generates constituency parse trees of texts, recursively composes representations for constituents, and matches them with images. We define concreteness of constituents by their matching scores with images, and use it to guide the parsing of text. Experiments on the MSCOCO data set show that VG-NSL outperforms various unsupervised parsing approaches that do not use visual grounding, in terms of F1 scores against gold parse trees. We find that VGNSL is much more stable with respect to the choice of random initialization and the amount of training data. We also find that the concreteness acquired by VG-NSL correlates well with a similar measure defined by linguists. Finally, we also apply VG-NSL to multiple languages in the Multi30K data set, showing that our model consistently outperforms prior unsupervised approaches.

Includes members:
  Member "unsupervised recurrent neural network grammars":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "are pre trained language models aware of phrases simple but strong baselines for grammar induction")
  Member "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the next decade in ai four steps towards robust artificial intelligence")
  Member "cooperative learning of disjoint syntax and semantics":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "are pre trained language models aware of phrases simple but strong baselines for grammar induction")
  Member "on tree based neural sentence modeling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "tree structured attention with hierarchical accumulation")
  Member "quantifying the visual concreteness of words and topics in multimodal datasets":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unsupervised discovery of multimodal links in multi image multi sentence documents")
  Member "visual coreference resolution in visual dialog using neural module networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "vision dialog navigation by exploring cross modal memory")
  Member "learning visually grounded semantics from contrastive adversarial samples":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision")
  Member "straight to the tree constituency parsing with neural syntactic distance":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "are pre trained language models aware of phrases simple but strong baselines for grammar induction")

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)