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: "part aware fine grained object categorization using weakly supervised part detection network"
  Abstract: Fine-grained object categorization aims for distinguishing objects of subordinate categories that belong to the same entry-level object category. The task is challenging due to the facts that (1) training images with ground-truth labels are difficult to obtain, and (2) variations among different subordinate categories are subtle. It is well established that characterizing features of different subordinate categories are located on local parts of object instances. In fact, careful part annotations are available in many fine-grained categorization datasets. However, manually annotating object parts requires expertise, which is also difficult to generalize to new fine-grained categorization tasks. In this work, we propose a Weakly Supervised Part Detection Network (PartNet) that is able to detect discriminative local parts for use of fine-grained categorization. A vanilla PartNet builds on top of a base subnetwork two parallel streams of upper network layers, which respectively compute scores of classification probabilities (over subordinate categories) and detection probabilities (over a specified number of discriminative part detectors) for local regions of interest (RoIs). The image-level prediction is obtained by aggregating element-wise products of these region-level probabilities. To generate a diverse set of RoIs as inputs of PartNet, we propose a simple Discretized Part Proposals module (DPP) that directly targets for proposing candidates of discriminative local parts, with no bridging via object-level proposals. Experiments on the benchmark CUB-200-2011 and Oxford Flower 102 datasets show the efficacy of our proposed method for both discriminative part detection and fine-grained categorization. In particular, we achieve the new state-of-the-art performance on CUB-200-2011 dataset when ground-truth part annotations are not available.

Connected through hyperedge HE-1 (induced by source paper "unsupervised multi class domain adaptation theory algorithms and practice"):
  with members:
    - "contrastive adaptation network for unsupervised domain adaptation"
    - "orthogonal deep neural networks"
    - "unsupervised domain adaptation via regularized conditional alignment"
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "beyond sharing weights for deep domain adaptation"
    - "domain symmetric networks for adversarial domain adaptation"
    - "bridging theory and algorithm for domain adaptation"
    - "learning to transfer examples for partial domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "unsupervised domain adaptation via structurally regularized deep clustering"):
  with members:
    - "unsupervised multi class domain adaptation theory algorithms and practice"
    - "discriminative adversarial domain adaptation"
    - "contrastive adaptation network for unsupervised domain adaptation"
    - "orthogonal deep neural networks"
    - "unsupervised domain adaptation via regularized conditional alignment"
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "domain specific batch normalization for unsupervised domain adaptation"
    - "domain symmetric networks for adversarial domain adaptation"

Connected through hyperedge HE-3 (induced by source paper "improving semantic analysis on point clouds via auxiliary supervision of local geometric priors"):
  with members:
    - "geonet deep geodesic networks for point cloud analysis"
    - "geometry aware generation of adversarial and cooperative point clouds"
    - "orthogonal deep neural networks"
    - "learning graph embedding with adversarial training methods"
    - "revisiting self supervised visual representation learning"
    - "self supervised learning of geometrically stable features through probabilistic introspection"
    - "so net self organizing network for point cloud analysis"
    - "dynamic graph cnn for learning on point clouds"

Connected through hyperedge HE-4 (induced by source paper "sampling techniques for large scale object detection from sparsely annotated objects"):
  with members:
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "soft sampling for robust object detection"
    - "solution for large scale hierarchical object detection datasets with incomplete annotation and data imbalance"
    - "couplenet coupling global structure with local parts for object detection"
    - "focal loss for dense object detection"
    - "transfer learning by ranking for weakly supervised object annotation"
    - "squeeze and excitation networks"
    - "chainercv a library for deep learning in computer vision"

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)