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 "robust change captioning"):
  Abstract: Describing what has changed in a scene can be useful to a user, but only if generated text focuses on what is semantically relevant. It is thus important to distinguish distractors (e.g. a viewpoint change) from relevant changes (e.g. an object has moved). We present a novel Dual Dynamic Attention Model (DUDA) to perform robust Change Captioning. Our model learns to distinguish distractors from semantic changes, localize the changes via Dual Attention over "before" and "after" images, and accurately describe them in natural language via Dynamic Speaker, by adaptively focusing on the necessary visual inputs (e.g. "before" or "after" image). To study the problem in depth, we collect a CLEVR-Change dataset, built off the CLEVR engine, with 5 types of scene changes. We benchmark a number of baselines on our dataset, and systematically study different change types and robustness to distractors. We show the superiority of our DUDA model in terms of both change captioning and localization. We also show that our approach is general, obtaining state-of-the-art results on the recent realistic Spot-the-Diff dataset which has no distractors.

Includes members:
  Member "clevr dialog a diagnostic dataset for multi round reasoning in visual dialog":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "guessing state tracking for visual dialogue")
  Member "clevr ref diagnosing visual reasoning with referring expressions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cops ref a new dataset and task on compositional referring expression comprehension")
  Member "grounding visual explanations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "explaining with counter visual attributes and examples")
  Member "multimodal explanations justifying decisions and pointing to the evidence":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "robust explanations for visual question answering")
  Member "transparency by design closing the gap between performance and interpretability in visual reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "vulnerabilities of connectionist ai applications evaluation and defence")
  Member "neural baby talk":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "aitpr attribute interaction tensor product representation for image caption")
  Member "discriminability objective for training descriptive captions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "reasoning visual dialogs with structural and partial observations")
  Member "learning to describe differences between pairs of similar images":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "adjusting image attributes of localized regions with low level dialogue")

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)