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: "from benedict cumberbatch to sherlock holmes character identification in tv series without a script"
  Abstract: The goal of this paper is the automatic identification of characters in TV and feature film material. In contrast to standard approaches to this task, which rely on the weak supervision afforded by transcripts and subtitles, we propose a new method requiring only a cast list. This list is used to obtain images of actors from freely available sources on the web, providing a form of partial supervision for this task. In using images of actors to recognize characters, we make the following three contributions: (i) We demonstrate that an automated semi-supervised learning approach is able to adapt from the actor's face to the character's face, including the face context of the hair; (ii) By building voice models for every character, we provide a bridge between frontal faces (for which there is plenty of actor-level supervision) and profile (for which there is very little or none); and (iii) by combining face context and speaker identification, we are able to identify characters with partially occluded faces and extreme facial poses. Results are presented on the TV series 'Sherlock' and the feature film 'Casablanca'. We achieve the state-of-the-art on the Casablanca benchmark, surpassing previous methods that have used the stronger supervision available from transcripts.

Connected through hyperedge HE-1 (induced by source paper "knowit vqa answering knowledge based questions about videos"):
  with members:
    - "passage re ranking with bert"
    - "ok vqa a visual question answering benchmark requiring external knowledge"
    - "from recognition to cognition visual commonsense reasoning"
    - "r vqa learning visual relation facts with semantic attention for visual question answering"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "tvqa localized compositional video question answering"
    - "mutan multimodal tucker fusion for visual question answering"
    - "whodunnit crime drama as a case for natural language understanding"

Connected through hyperedge HE-2 (induced by source paper "a graph based framework to bridge movies and synopses"):
  with members:
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "person search in videos with one portrait through visual and temporal links"
    - "a joint sequence fusion model for video question answering and retrieval"
    - "from trailers to storylines an efficient way to learn from movies"
    - "multilevel language and vision integration for text to clip retrieval"
    - "unifying identification and context learning for person recognition"
    - "holistic multi modal memory network for movie question answering"
    - "dual encoding for zero example video retrieval"

Connected through hyperedge HE-3 (induced by source paper "video face clustering with unknown number of clusters"):
  with members:
    - "centroid based deep metric learning for speaker recognition"
    - "self supervised learning of face representations for video face clustering"
    - "learnable pins cross modal embeddings for person identity"
    - "tvqa localized compositional video question answering"
    - "normface l 2 hypersphere embedding for face verification"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "generating descriptions with grounded and co referenced people"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-4 (induced by source paper "count crop and recognise fine grained recognition in the wild"):
  with members:
    - "see better before looking closer weakly supervised data augmentation network for fine grained visual classification"
    - "the via annotation software for images audio and video"
    - "face recognition primates in the wild"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "c wsl count guided weakly supervised localization"
    - "the inaturalist species classification and detection dataset"
    - "seed expand and constrain three principles for weakly supervised image segmentation"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-5 (induced by source paper "self supervised learning of face representations for video face clustering"):
  with members:
    - "unsupervised learning of face representations"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "generating descriptions with grounded and co referenced people"
    - "classification driven dynamic image enhancement"
    - "a pose sensitive embedding for person re identification with expanded cross neighborhood re ranking"
    - "moviegraphs towards understanding human centric situations from videos"
    - "learning from video and text via large scale discriminative clustering"
    - "shuffle and learn unsupervised learning using temporal order verification"

Connected through hyperedge HE-6 (induced by source paper "large scale multi modal person identification in real unconstrained environments"):
  with members:
    - "iqiyi vid a large dataset for multi modal person identification"
    - "yolov3 an incremental improvement"
    - "arcface additive angular margin loss for deep face recognition"
    - "pyramidbox a context assisted single shot face detector"
    - "voxceleb2 deep speaker recognition"
    - "sphereface deep hypersphere embedding for face recognition"
    - "l2 constrained softmax loss for discriminative face verification"
    - "voxceleb a large scale speaker identification dataset"

Connected through hyperedge HE-7 (induced by source paper "iqiyi vid a large dataset for multi modal person identification"):
  with members:
    - "improving person re identification by attribute and identity learning"
    - "person search in videos with one portrait through visual and temporal links"
    - "the devil of face recognition is in the noise"
    - "arcface additive angular margin loss for deep face recognition"
    - "pyramidbox a context assisted single shot face detector"
    - "voxceleb2 deep speaker recognition"
    - "crowdhuman a benchmark for detecting human in a crowd"
    - "video based person re identification with accumulative motion context"

Connected through hyperedge HE-8 (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"
    - "objects that sound"
    - "in defense of the triplet loss for person re identification"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "improving speaker turn embedding by crossmodal transfer learning from face embedding"

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)