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: "unifying identification and context learning for person recognition"
  Abstract: Despite the great success of face recognition techniques, recognizing persons under unconstrained settings remains challenging. Issues like profile views, unfavorable lighting, and occlusions can cause substantial difficulties. Previous works have attempted to tackle this problem by exploiting the context, e.g. clothes and social relations. While showing promising improvement, they are usually limited in two important aspects, relying on simple heuristics to combine different cues and separating the construction of context from people identities. In this work, we aim to move beyond such limitations and propose a new framework to leverage context for person recognition. In particular, we propose a Region Attention Network, which is learned to adaptively combine visual cues with instance-dependent weights. We also develop a unified formulation, where the social contexts are learned along with the reasoning of people identities. These models substantially improve the robustness when working with the complex contextual relations in unconstrained environments. On two large datasets, PIPA and Cast In Movies (CIM), a new dataset proposed in this work, our method consistently achieves state-of-the-art performance under multiple evaluation policies.

Connected through hyperedge HE-1 (induced by source paper "learning context graph for person search"):
  with members:
    - "person re identification with deep similarity guided graph neural network"
    - "person search via a mask guided two stream cnn model"
    - "learning human object interactions by graph parsing neural networks"
    - "video salient object detection via fully convolutional networks"
    - "videos as space time region graphs"
    - "person re identification via recurrent feature aggregation"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "beyond triplet loss a deep quadruplet network for person re identification"

Connected through hyperedge HE-2 (induced by source paper "wider face and pedestrian challenge 2018 methods and results"):
  with members:
    - "person search in videos with one portrait through visual and temporal links"
    - "group normalization"
    - "arcface additive angular margin loss for deep face recognition"
    - "ring loss convex feature normalization for face recognition"
    - "sface an efficient network for face detection in large scale variations"
    - "soft nms improving object detection with one line of code"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "face attention network an effective face detector for the occluded faces"

Connected through hyperedge HE-3 (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"
    - "holistic multi modal memory network for movie question answering"
    - "dual encoding for zero example video retrieval"
    - "from benedict cumberbatch to sherlock holmes character identification in tv series without a script"

Connected through hyperedge HE-4 (induced by source paper "person search in videos with one portrait through visual and temporal links"):
  with members:
    - "person recognition in personal photo collections"
    - "person search with natural language description"
    - "detecting temporally consistent objects in videos through object class label propagation"
    - "deep attributes driven multi camera person re identification"
    - "learning deep feature representations with domain guided dropout for person re identification"
    - "a comprehensive evaluation and benchmark for person re identification features metrics and datasets"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "joint detection and identification feature learning for person search"

Connected through hyperedge HE-5 (induced by source paper "query adaptive late fusion for image retrieval"):
  with members:
    - "person recognition in personal photo collections"
    - "learning deep features via congenerous cosine loss for person recognition"
    - "sphereface deep hypersphere embedding for face recognition"
    - "glad global local alignment descriptor for pedestrian retrieval"
    - "end to end learning of deep visual representations for image retrieval"
    - "deep image retrieval learning global representations for image search"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "deep residual learning for image recognition"

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)