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: "when unsupervised domain adaptation meets tensor representations"
  Abstract: Domain adaption (DA) allows machine learning methods trained on data sampled from one distribution to be applied to data sampled from another. It is thus of great practical importance to the application of such methods. Despite the fact that tensor representations are widely used in Computer Vision to capture multi-linear relationships that affect the data, most existing DA methods are applicable to vectors only. This renders them incapable of reflecting and preserving important structure in many problems. We thus propose here a learning-based method to adapt the source and target tensor representations directly, without vectorization. In particular, a set of alignment matrices is introduced to align the tensor representations from both domains into the invariant tensor subspace. These alignment matrices and the tensor subspace are modeled as a joint optimization problem and can be learned adaptively from the data using the proposed alternative minimization scheme. Extensive experiments show that our approach is capable of preserving the discriminative power of the source domain, of resisting the effects of label noise, and works effectively for small sample sizes, and even one-shot DA. We show that our method outperforms the state-of-the-art on the task of cross-domain visual recognition in both efficacy and efficiency, and particularly that it outperforms all comparators when applied to DA of the convolutional activations of deep convolutional networks.

Connected through hyperedge HE-1 (induced by source paper "from open set to closed set supervised spatial divide and conquer for object counting"):
  with members:
    - "bayesian loss for crowd count estimation with point supervision"
    - "learning spatial awareness to improve crowd counting"
    - "learn to scale generating multipolar normalized density map for crowd counting"
    - "pushing the frontiers of unconstrained crowd counting new dataset and benchmark method"
    - "from open set to closed set counting objects by spatial divide and conquer"
    - "divide and grow capturing huge diversity in crowd images with incrementally growing cnn"
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "crowd counting using deep recurrent spatial aware network"

Connected through hyperedge HE-2 (induced by source paper "few shot adaptive faster r cnn"):
  with members:
    - "few example object detection with model communication"
    - "semantic foggy scene understanding with synthetic data"
    - "cross domain weakly supervised object detection through progressive domain adaptation"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "lstd a low shot transfer detector for object detection"
    - "focal loss for dense object detection"
    - "few shot adversarial domain adaptation"
    - "cascade r cnn delving into high quality object detection"

Connected through hyperedge HE-3 (induced by source paper "open set domain adaptation for image and action recognition"):
  with members:
    - "mind the class weight bias weighted maximum mean discrepancy for unsupervised domain adaptation"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "deep hashing network for unsupervised domain adaptation"
    - "adversarial discriminative domain adaptation"
    - "unified deep supervised domain adaptation and generalization"
    - "the kinetics human action video dataset"
    - "visda the visual domain adaptation challenge"
    - "joint geometrical and statistical alignment for visual domain adaptation"

Connected through hyperedge HE-4 (induced by source paper "tkd temporal knowledge distillation for active perception"):
  with members:
    - "a novel design of adaptive and hierarchical convolutional neural networks using partial reconfiguration on fpga"
    - "real world anomaly detection in surveillance videos"
    - "object detection at 200 frames per second"
    - "yolov3 an incremental improvement"
    - "gaple generalizable approaching policy learning for robotic object searching in indoor environment"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "online model distillation for efficient video inference"
    - "dark model adaptation semantic image segmentation from daytime to nighttime"

Connected through hyperedge HE-5 (induced by source paper "adapting convolutional neural networks for geographical domain shift"):
  with members:
    - "albumentations fast and flexible image augmentations"
    - "are all training examples created equal an empirical study"
    - "deep visual domain adaptation a survey"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "progressive neural architecture search"
    - "learning transferable architectures for scalable image recognition"
    - "cycada cycle consistent adversarial domain adaptation"
    - "don t decay the learning rate increase the batch size"

Connected through hyperedge HE-6 (induced by source paper "adversarial transfer learning for cross domain visual recognition"):
  with members:
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "generate to adapt aligning domains using generative adversarial networks"
    - "dualgan unsupervised dual learning for image to image translation"
    - "unsupervised pixel level domain adaptation with generative adversarial networks"
    - "improved techniques for training gans"
    - "deep transfer learning with joint adaptation networks"
    - "coupled generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "structured discriminative tensor dictionary learning for unsupervised domain adaptation"):
  with members:
    - "deep micro dictionary learning and coding network"
    - "deep cocktail network multi source unsupervised domain adaptation with category shift"
    - "cycada cycle consistent adversarial domain adaptation"
    - "maximum classifier discrepancy for unsupervised domain adaptation"
    - "adversarial discriminative domain adaptation"
    - "adversarial feature augmentation for unsupervised domain adaptation"
    - "joint geometrical and statistical alignment for visual domain adaptation"
    - "learning an invariant hilbert space for domain adaptation"

Connected through hyperedge HE-8 (induced by source paper "learning semantic segmentation from synthetic data a geometrically guided input output adaptation approach"):
  with members:
    - "penalizing top performers conservative loss for semantic segmentation adaptation"
    - "learning to adapt structured output space for semantic segmentation"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"
    - "dlow domain flow for adaptation and generalization"
    - "spigan privileged adversarial learning from simulation"
    - "image to image translation for domain adaptation"
    - "analyzing modular cnn architectures for joint depth prediction and semantic segmentation"

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)