human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "improving speaker turn embedding by crossmodal transfer learning from face embedding"
  Abstract: Learning speaker turn embeddings has shown considerable improvement in situations where conventional speaker modeling approaches fail. However, this improvement is relatively limited when compared to the gain observed in face embedding learning, which has been proven very successful for face verification and clustering tasks. Assuming that face and voices from the same identities share some latent properties (like age, gender, ethnicity), we propose three transfer learning approaches to leverage the knowledge from the face domain (learned from thousands of images and identities) for tasks in the speaker domain. These approaches, namely target embedding transfer, relative distance transfer, and clustering structure transfer, utilize the structure of the source face embedding space at different granularities to regularize the target speaker turn embedding space as optimizing terms. Our methods are evaluated on two public broadcast corpora and yield promising advances over competitive baselines in verification and audio clustering tasks, especially when dealing with short speaker utterances. The analysis of the results also gives insight into characteristics of the embedding spaces and shows their potential applications.

Connected through hyperedge HE-1 (induced by source paper "learnable pins cross modal embeddings for person identity"):
  with members:
    - "seeing voices and hearing faces cross modal biometric matching"
    - "self supervised learning of a facial attribute embedding from video"
    - "emotion recognition in speech using cross modal transfer in the wild"
    - "voxceleb2 deep speaker recognition"
    - "from benedict cumberbatch to sherlock holmes character identification in tv series without a script"
    - "objects that sound"
    - "in defense of the triplet loss for person re identification"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-2 (induced by source paper "seeing voices and hearing faces cross modal biometric matching"):
  with members:
    - "look listen and learn"
    - "voxceleb a large scale speaker identification dataset"
    - "cross modal scene networks"
    - "ambient sound provides supervision for visual learning"
    - "lip reading sentences in the wild"
    - "action recognition with dynamic image networks"
    - "soundnet learning sound representations from unlabeled video"
    - "self supervised video representation learning with odd one out networks"

Connected through hyperedge HE-3 (induced by source paper "theoretical guarantees of deep embedding losses under label noise"):
  with members:
    - "mining on manifolds metric learning without labels"
    - "exploring the limits of weakly supervised pretraining"
    - "the resistance to label noise in k nn and cnn depends on its concentration"
    - "on the resistance of neural nets to label noise"
    - "sampling matters in deep embedding learning"
    - "transitive invariance for self supervised visual representation learning"
    - "unsupervised learning of semantic audio representations"
    - "voxceleb a large scale speaker identification dataset"

Question: Please tell me which class the center node 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)