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: "constrained speaker diarization of tv series based on visual patterns"
  Abstract: Speaker diarization, usually denoted as the “who spoke when” task, turns out to be particularly challenging when applied to fictional films, where many characters talk in various acoustic conditions (background music, sound effects...). Despite this acoustic variability, such movies exhibit specific visual patterns in the dialogue scenes. In this paper, we introduce a two-step method to achieve speaker diarization in TV series: a speaker diarization is first performed locally in the scenes detected as dialogues; then, the hypothesized local speakers are merged in a second agglomerative clustering process, with the constraint that speakers locally hypothesized to be distinct must not be assigned to the same cluster. The performances of our approach are compared to those obtained by standard speaker diarization tools applied to the same data.

Connected through hyperedge HE-1 (induced by source paper "serial speakers a dataset of tv series"):
  with members:
    - "remembering winter was coming character oriented video summaries of tv series"
    - "extraction and analysis of fictional character networks a survey"
    - "audiovisual speaker diarization of tv series"

Connected through hyperedge HE-2 (induced by source paper "extraction and analysis of fictional character networks a survey"):
  with members:
    - "remembering winter was coming character oriented video summaries of tv series"
    - "conversational networks for automatic online moderation"
    - "remembering winter was coming"
    - "predicting kills in game of thrones using network properties"
    - "multilayer network model of movie script"
    - "from word to sense embeddings a survey on vector representations of meaning"
    - "i will be there for you six friends in a clique"
    - "the one comparing narrative social network extraction techniques"

Connected through hyperedge HE-3 (induced by source paper "speaker naming in movies"):
  with members:
    - "a read write memory network for movie story understanding"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "end to end memory networks"
    - "deep multimodal speaker naming"
    - "skip thought vectors"
    - "movieqa understanding stories in movies through question answering"
    - "distributed representations of words and phrases and their compositionality"
    - "scikit learn machine learning in python"

Connected through hyperedge HE-4 (induced by source paper "extraction and analysis of dynamic conversational networks from tv series"):
  with members:
    - "remembering winter was coming character oriented video summaries of tv series"
    - "remembering winter was coming"
    - "narrative smoothing dynamic conversational network for the analysis of tv series plots"
    - "audiovisual speaker diarization of tv series"

Connected through hyperedge HE-5 (induced by source paper "improving speaker turn embedding by crossmodal transfer learning from face embedding"):
  with members:
    - "cp mtml coupled projection multi task metric learning for large scale face retrieval"
    - "tristounet triplet loss for speaker turn embedding"
    - "look listen and learn a multimodal lstm for speaker identification"
    - "deep multimodal speaker naming"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "learning face representation from scratch"

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.MM(Multimedia)