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 "meta module network for compositional visual reasoning"):
  Abstract: There are two main lines of research on visual reasoning: neural module network (NMN) with explicit multi-hop reasoning through handcrafted neural modules, and monolithic network with implicit reasoning in the latent feature space. The former excels in interpretability and compositionality, while the latter usually achieves better performance due to model flexibility and parameter efficiency. In order to bridge the gap between the two and leverage the merits of both, we present Meta Module Network (MMN), a novel hybrid approach that can utilize a Meta Module to perform versatile functionalities, while preserving compositionality and interpretability through modularized design. The proposed model first parses an input question into a functional program through a Program Generator. Instead of handcrafting a task-specific network to represent each function similar to traditional NMN, we propose a Meta Module, which can read a recipe (function specifications) to dynamically instantiate the task-specific Instance Modules for compositional reasoning. To endow different instance modules with designated functionalities, we design a symbolic teacher which can execute against provided scene graphs to generate guidelines for the instantiated modules (student) to follow during training. Experiments conducted on the GQA benchmark demonstrates that MMN outperforms both NMN and monolithic network baselines, with good generalization ability to handle unseen functions.

Includes members:
  Member "knowledge embedded routing network for scene graph generation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "unbiased scene graph generation from biased training")
  Member "lxmert learning cross modality encoder representations from transformers":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "bert can see out of the box on the cross modal transferability of text representations")
  Member "uniter learning universal image text representations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "in defense of grid features for visual question answering")
  Member "learning by abstraction the neural state machine":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unbiased scene graph generation from biased training")
  Member "language conditioned graph networks for relational reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unshuffling data for improved generalization")
  Member "gqa a new dataset for real world visual reasoning and compositional question answering":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "unbiased scene graph generation from biased training")
  Member "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "bert can see out of the box on the cross modal transferability of text representations")
  Member "probabilistic neural symbolic models for interpretable visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "toward interpretability of dual encoder models for dialogue response suggestions")

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.CV(Computer Vision and Pattern Recognition)