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: "hierarchical adaptive structural svm for domain adaptation"
  Abstract: A key topic in classification is the accuracy loss produced when the data distribution in the training (source) domain differs from that in the testing (target) domain. This is being recognized as a very relevant problem for many computer vision tasks such as image classification, object detection, and object category recognition. In this paper, we present a novel domain adaptation method that leverages multiple target domains (or sub-domains) in a hierarchical adaptation tree. The core idea is to exploit the commonalities and differences of the jointly considered target domains. #R##N#Given the relevance of structural SVM (SSVM) classifiers, we apply our idea to the adaptive SSVM (A-SSVM), which only requires the target domain samples together with the existing source-domain classifier for performing the desired adaptation. Altogether, we term our proposal as hierarchical A-SSVM (HA-SSVM). #R##N#As proof of concept we use HA-SSVM for pedestrian detection and object category recognition. In the former we apply HA-SSVM to the deformable part-based model (DPM) while in the latter HA-SSVM is applied to multi-category classifiers. In both cases, we show how HA-SSVM is effective in increasing the detection/recognition accuracy with respect to adaptation strategies that ignore the structure of the target data. Since, the sub-domains of the target data are not always known a priori, we shown how HA-SSVM can incorporate sub-domain structure discovery for object category recognition.

Connected through hyperedge HE-1 (induced by source paper "a curriculum domain adaptation approach to the semantic segmentation of urban scenes"):
  with members:
    - "fully convolutional adaptation networks for semantic segmentation"
    - "penalizing top performers conservative loss for semantic segmentation adaptation"
    - "learning to adapt structured output space for semantic segmentation"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"
    - "a domain agnostic normalization layer for unsupervised adversarial domain adaptation"
    - "effective use of synthetic data for urban scene semantic segmentation"
    - "two at once enhancing learning and generalization capacities via ibn net"
    - "image to image translation for domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "a general approach to domain adaptation with applications in astronomy"):
  with members:
    - "frustratingly easy domain adaptation"
    - "domain adaptation learning bounds and algorithms"
    - "domain adaptation for statistical classifiers"
    - "active learning with statistical models"

Connected through hyperedge HE-3 (induced by source paper "domain adaptation for visual applications a comprehensive survey"):
  with members:
    - "beyond sharing weights for deep domain adaptation"
    - "generalized multi view embedding for visual recognition and cross modal retrieval"
    - "adversarial discriminative domain adaptation"
    - "unsupervised pixel level domain adaptation with generative adversarial networks"
    - "virtual worlds as proxy for multi object tracking analysis"
    - "play and learn using video games to train computer vision models"
    - "deep transfer learning with joint adaptation networks"
    - "lightweight unsupervised domain adaptation by convolutional filter reconstruction"

Connected through hyperedge HE-4 (induced by source paper "curriculum domain adaptation for semantic segmentation of urban scenes"):
  with members:
    - "synthetic to real adaptation with generative correlation alignment networks"
    - "training constrained deconvolutional networks for road scene semantic segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "harnessing deep neural networks with logic rules"
    - "unsupervised pixel level domain adaptation with generative adversarial networks"
    - "learning from simulated and unsupervised images through adversarial training"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "play and learn using video games to train computer vision models"

Connected through hyperedge HE-5 (induced by source paper "road reality oriented adaptation for semantic segmentation of urban scenes"):
  with members:
    - "learning to adapt structured output space for semantic segmentation"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "no more discrimination cross city adaptation of road scene segmenters"
    - "maximum classifier discrepancy for unsupervised domain adaptation"
    - "when unsupervised domain adaptation meets tensor representations"
    - "synthetic to real adaptation with generative correlation alignment networks"
    - "unified deep supervised domain adaptation and generalization"
    - "curriculum domain adaptation for semantic segmentation of urban scenes"

Connected through hyperedge HE-6 (induced by source paper "from virtual to real world visual perception using domain adaptation the dpm as example"):
  with members:
    - "how useful is photo realistic rendering for visual learning"
    - "training constrained deconvolutional networks for road scene semantic segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "virtual worlds as proxy for multi object tracking analysis"
    - "play and learn using video games to train computer vision models"
    - "target driven visual navigation in indoor scenes using deep reinforcement learning"
    - "model driven simulations for deep convolutional neural networks"
    - "semantic pose using deep networks trained on synthetic rgb d"

Connected through hyperedge HE-7 (induced by source paper "3d object proposals using stereo imagery for accurate object class detection"):
  with members:
    - "vehicle detection from 3d lidar using fully convolutional network"
    - "fast detection of multiple objects in traffic scenes with a common detection framework"
    - "what makes for effective detection proposals"
    - "sequential optimization for efficient high quality object proposal generation"
    - "learning to detect vehicles by clustering appearance patterns"
    - "filtered channel features for pedestrian detection"
    - "towards scene understanding with detailed 3d object representations"
    - "segdeepm exploiting segmentation and context in deep neural networks for object detection"

Connected through hyperedge HE-8 (induced by source paper "node adapt path adapt and tree adapt model transfer domain adaptation for random forest"):
  with members:
    - "human pose estimation from rgb input using synthetic training data"
    - "lsda large scale detection through adaptation"

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)