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: "group re identification via unsupervised transfer of sparse features encoding"
  Abstract: Person re-identification is best known as the problem of associating a single person that is observed from one or more disjoint cameras. The existing literature has mainly addressed such an issue, neglecting the fact that people usually move in groups, like in crowded scenarios. We believe that the additional information carried by neighboring individuals provides a relevant visual context that can be exploited to obtain a more robust match of single persons within the group. Despite this, re-identifying groups of people compound the common single person re-identification problems by introducing changes in the relative position of persons within the group and severe self-occlusions. In this paper, we propose a solution for group re-identification that grounds on transferring knowledge from single person re-identification to group re-identification by exploiting sparse dictionary learning. First, a dictionary of sparse atoms is learned using patches extracted from single person images. Then, the learned dictionary is exploited to obtain a sparsity-driven residual group representation, which is finally matched to perform the re-identification. Extensive experiments on the i-LIDS groups and two newly collected datasets show that the proposed solution outperforms state-of-the-art approaches.

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"
    - "unifying identification and context learning for person recognition"
    - "person re identification via recurrent feature aggregation"
    - "a discriminatively learned cnn embedding for person reidentification"

Connected through hyperedge HE-2 (induced by source paper "deep set to set matching and learning"):
  with members:
    - "group reidentification with multigrained matching and integration"
    - "dressing as a whole outfit compatibility learning based on node wise graph neural networks"
    - "set transformer a framework for attention based permutation invariant neural networks"
    - "learning type aware embeddings for fashion compatibility"
    - "deep sets"
    - "recommending outfits from personal closet"
    - "fashionnet personalized outfit recommendation with deep neural network"
    - "outfit generation and style extraction via bidirectional lstm and autoencoder"

Connected through hyperedge HE-3 (induced by source paper "video based convolutional attention for person re identification"):
  with members:
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "video based person re identification using spatial temporal attention networks"
    - "person re identification via recurrent feature aggregation"
    - "quality aware network for set to set recognition"
    - "end to end comparative attention networks for person re identification"
    - "jointly attentive spatial temporal pooling networks for video based person re identification"
    - "multi scale deep learning architectures for person re identification"
    - "learning a discriminative null space for person re identification"

Connected through hyperedge HE-4 (induced by source paper "group re identification via transferred single and couple representation learning"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "camera style adaptation for person re identification"
    - "person re identification by deep joint learning of multi loss classification"
    - "part based deep hashing for large scale person re identification"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "pose driven deep convolutional model for person re identification"
    - "pose invariant embedding for deep person re identification"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-5 (induced by source paper "distance based camera network topology inference for person re identification"):
  with members:
    - "3 d personvlad learning deep global representations for video based person reidentification"
    - "deep adaptive feature embedding with local sample distributions for person re identification"
    - "what and where to match deep spatially multiplicative integration networks for person re identification"
    - "where and when to look deep siamese attention networks for video based person re identification"
    - "unified framework for automated person re identification and camera network topology inference in camera networks"
    - "structured deep hashing with convolutional neural networks for fast person re identification"
    - "deep attributes driven multi camera person re identification"
    - "learning deep feature representations with domain guided dropout for person re identification"

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)