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 "referring expression grounding by marginalizing scene graph likelihood"):
  Abstract: We focus on the task of grounding referring expressions in images, e.g., localizing "the white truck in front of a yellow one". To resolve this task fundamentally, one should first find out the contextual objects (e.g., the "yellow" truck) and then exploit them to disambiguate the referent from other similar objects, by using the attributes and relationships (e.g., "white", "yellow", "in front of"). However, it is extremely challenging to train such a model as the ground-truth of the contextual objects and their relationships are usually missing due to the prohibitive annotation cost. Therefore, nearly all existing methods attempt to evade the above joint grounding and reasoning process, but resort to a holistic association between the sentence and region feature. As a result, they suffer from heavy parameters of fully-connected layers, poor interpretability, and limited generalization to unseen expressions. In this paper, we tackle this challenge by training and inference with the proposed Marginalized Scene Graph Likelihood (MSGL). Specifically, we use scene graph: a graphical representation parsed from the referring expression, where the nodes are objects with attributes and the edges are relationships. Thanks to the conditional random field (CRF) built on scene graph, we can ground every object to its corresponding region, and perform reasoning with the unlabeled contexts by marginalizing out them using the sum-product belief propagation. Overall, our proposed MSGL is effective and interpretable, e.g., on three benchmarks, MSGL consistently outperforms the state-of-the-arts while offers a complete grounding of all the objects in a sentence.

Includes members:
  Member "learning to compose and reason with language tree structures for visual grounding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "scanrefer 3d object localization in rgb d scans using natural language")
  Member "using syntax to ground referring expressions in natural images":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "align2ground weakly supervised phrase grounding guided by image caption alignment")
  Member "touchdown natural language navigation and spatial reasoning in visual street environments":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mvp unified motion and visual self supervised learning for large scale robotic navigation")
  Member "neighbourhood watch referring expression comprehension via language guided graph attention networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi task collaborative network for joint referring expression comprehension and segmentation")
  Member "mattnet modular attention network for referring expression comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "rethinking diversified and discriminative proposal generation for visual grounding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi task collaborative network for joint referring expression comprehension and segmentation")
  Member "relational inductive biases deep learning and graph networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "visual question reasoning on general dependency tree":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "relationship embedded representation learning for grounding referring expressions")

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)