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 "perfect match improved cross modal embeddings for audio visual synchronisation"):
  Abstract: This paper proposes a new strategy for learning powerful cross-modal embeddings for audio-to-video synchronization. Here, we set up the problem as one of cross-modal retrieval, where the objective is to find the most relevant audio segment given a short video clip. The method builds on the recent advances in learning representations from cross-modal self-supervision. #R##N#The main contributions of this paper are as follows: (1) we propose a new learning strategy where the embeddings are learnt via a multi-way matching problem, as opposed to a binary classification (matching or non-matching) problem as proposed by recent papers; (2) we demonstrate that performance of this method far exceeds the existing baselines on the synchronization task; (3) we use the learnt embeddings for visual speech recognition in self-supervision, and show that the performance matches the representations learnt end-to-end in a fully-supervised manner.

Includes members:
  Member "learning to localize sound source in visual scenes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "stavis spatio temporal audiovisual saliency network")
  Member "seeing voices and hearing faces cross modal biometric matching":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unsupervised any to many audiovisual synthesis via exemplar autoencoders")
  Member "dynamic temporal alignment of speech to lips":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "alignnet a unifying approach to audio visual alignment")
  Member "cooperative learning of audio and video models from self supervised synchronization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning")
  Member "audio visual scene analysis with self supervised multisensory features":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi modal self supervision from generalized data transformations")
  Member "on learning associations of faces and voices":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning to localize sound sources in visual scenes analysis and applications")
  Member "objects that sound":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi modal domain adaptation for fine grained action recognition")
  Member "lip reading sentences in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "lipreading using temporal convolutional networks")

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)