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 "what can computational models learn from human selective attention a review from an audiovisual crossmodal perspective"):
  Abstract: Selective attention plays an essential role in information acquisition and utilization from the environment. In the past 50 years, research on selective attention has been a central topic in cognitive science. Compared with unimodal studies, crossmodal studies are more complex but necessary to solve real-world challenges in both human experiments and computational modeling. Although an increasing number of findings on crossmodal selective attention have shed light on humans' behavioral patterns and neural underpinnings, a much better understanding is still necessary to yield the same benefit for computational intelligent agents. This article reviews studies of selective attention in unimodal visual and auditory and crossmodal audiovisual setups from the multidisciplinary perspectives of psychology and cognitive neuroscience, and evaluates different ways to simulate analogous mechanisms in computational models and robotics. We discuss the gaps between these fields in this interdisciplinary review and provide insights about how to use psychological findings and theories in artificial intelligence from different perspectives.

Includes members:
  Member "generating multiple objects at spatially distinct locations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "blockgan learning 3d object aware scene representations from unlabelled images")
  Member "a self attention based deep learning method for lesion attribute detection from ct reports":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "holistic and comprehensive annotation of clinically significant findings on diverse ct images learning from radiology reports and label ontology")
  Member "enhanced robot speech recognition using biomimetic binaural sound source localization":
    also connected through other hyperedges:
      (none)
  Member "representation similarity analysis for efficient task taxonomy transfer learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "depara deep attribution graph for deep knowledge transferability")
  Member "cross modal attentional context learning for rgb d object detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "visual analytics in deep learning an interrogative survey for the next frontiers":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "explainer a visual analytics framework for interactive and explainable machine learning")
  Member "a neurorobotic experiment for crossmodal conflict resolution in complex environments":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "continual lifelong learning with neural networks a review")
  Member "end to end saliency mapping via probability distribution prediction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "stavis spatio temporal audiovisual saliency network")

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)