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 "learning audio sheet music correspondences for score identification and offline alignment"):
  Abstract: This work addresses the problem of matching short excerpts of audio with their respective counterparts in sheet music images. We show how to employ neural network-based cross-modality embedding spaces for solving the following two sheet music-related tasks: retrieving the correct piece of sheet music from a database when given a music audio as a search query; and aligning an audio recording of a piece with the corresponding images of sheet music. We demonstrate the feasibility of this in experiments on classical piano music by five different composers (Bach, Haydn, Mozart, Beethoven and Chopin), and additionally provide a discussion on why we expect multi-modal neural networks to be a fruitful paradigm for dealing with sheet music and audio at the same time.

Includes members:
  Member "end to end cross modality retrieval with cca projections and pairwise ranking loss":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "object detection on single monocular images through canonical correlation analysis")
  Member "on the potential of simple framewise approaches to piano transcription":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the impact of audio input representations on neural network based music transcription")
  Member "towards score following in sheet music images":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "audio conditioned u net for position estimation in full sheet images")
  Member "batch normalization accelerating deep network training by reducing internal covariate shift":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "fast and accurate deep network learning by exponential linear units elus":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ride rewarding impact driven exploration for procedurally generated environments")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "very deep convolutional networks for large scale image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "unifying visual semantic embeddings with multimodal neural language models":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "adversarial ranking attack and defense")

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.IR(Information Retrieval)