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: "iqiyi vid a large dataset for multi modal person identification"
  Abstract: Person identification in the wild is very challenging due to great variation in poses, face quality, clothes, makeup and so on. Traditional research, such as face recognition, person re-identification, and speaker recognition, often focuses on a single modal of information, which is inadequate to handle all the situations in practice. Multi-modal person identification is a more promising way that we can jointly utilize face, head, body, audio features, and so on. In this paper, we introduce iQIYI-VID, the largest video dataset for multi-modal person identification. It is composed of 600K video clips of 5,000 celebrities. These video clips are extracted from 400K hours of online videos of various types, ranging from movies, variety shows, TV series, to news broadcasting. All video clips pass through a careful human annotation process, and the error rate of labels is lower than 0.2%. We evaluated the state-of-art models of face recognition, person re-identification, and speaker recognition on the iQIYI-VID dataset. Experimental results show that these models are still far from being perfect for task of person identification in the wild. We further demonstrate that a simple fusion of multi-modal features can improve person identification considerably. We have released the dataset online to promote multi-modal person identification research.

Connected through hyperedge HE-1 (induced by source paper "when person re identification meets changing clothes"):
  with members:
    - "joint discriminative and generative learning for person re identification"
    - "pyramidbox a context assisted single shot face detector"
    - "learning to detect and track visible and occluded body joints in a virtual world"
    - "domain adaptation through synthesis for unsupervised person re identification"
    - "part aligned bilinear representations for person re identification"
    - "looking beyond appearances synthetic training data for deep cnns in re identification"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"
    - "beyond part models person retrieval with refined part pooling and a strong convolutional baseline"

Connected through hyperedge HE-2 (induced by source paper "unknown identity rejection loss utilizing unlabeled data for face recognition"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "arcface additive angular margin loss for deep face recognition"
    - "deep co training for semi supervised image recognition"
    - "additive margin softmax for face verification"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-3 (induced by source paper "large scale multi modal person identification in real unconstrained environments"):
  with members:
    - "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"
    - "from benedict cumberbatch to sherlock holmes character identification in tv series without a script"
    - "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-4 (induced by source paper "arcface additive angular margin loss for deep face recognition"):
  with members:
    - "cosface large margin cosine loss for deep face recognition"
    - "rethinking feature distribution for loss functions in image classification"
    - "learning towards minimum hyperspherical energy"
    - "multicolumn networks for face recognition"
    - "additive margin softmax for face verification"
    - "comparator networks"
    - "face recognition via centralized coordinate learning"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

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)