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: "residual parameter transfer for deep domain adaptation"
  Abstract: The goal of Deep Domain Adaptation is to make it possible to use Deep Nets trained in one domain where there is enough annotated training data in another where there is little or none. Most current approaches have focused on learning feature representations that are invariant to the changes that occur when going from one domain to the other, which means using the same network parameters in both domains. While some recent algorithms explicitly model the changes by adapting the network parameters, they either severely restrict the possible domain changes, or significantly increase the number of model parameters. #R##N#By contrast, we introduce a network architecture that includes auxiliary residual networks, which we train to predict the parameters in the domain with little annotated data from those in the other one. This architecture enables us to flexibly preserve the similarities between domains where they exist and model the differences when necessary. We demonstrate that our approach yields higher accuracy than state-of-the-art methods without undue complexity.

Connected through hyperedge HE-1 (induced by source paper "learning to adapt class specific features across domains for semantic segmentation"):
  with members:
    - "learning to adapt structured output space for semantic segmentation"
    - "yolov3 an incremental improvement"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "on the iterative refinement of densely connected representation levels for semantic segmentation"
    - "importance weighted adversarial nets for partial domain adaptation"
    - "image to image translation for cross domain disentanglement"
    - "image to image translation for domain adaptation"
    - "unsupervised domain adaptation with similarity learning"

Connected through hyperedge HE-2 (induced by source paper "semi supervised unconstrained action unit detection via latent feature domain"):
  with members:
    - "look at boundary a boundary aware face alignment algorithm"
    - "deep adaptive attention for joint facial action unit detection and face alignment"
    - "self supervised learning of a facial attribute embedding from video"
    - "t2net synthetic to realistic translation for solving single image depth estimation tasks"
    - "facial action unit detection using attention and relation learning"
    - "adversarial discriminative domain adaptation"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "hand keypoint detection in single images using multiview bootstrapping"

Connected through hyperedge HE-3 (induced by source paper "transfer adaptation learning a decade survey"):
  with members:
    - "optimal projection guided transfer hashing for image retrieval"
    - "representation learning by rotating your faces"
    - "open set domain adaptation for image and action recognition"
    - "deep visual domain adaptation a survey"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "load balanced gans for multi view face image synthesis"
    - "learning domain invariant subspace using domain features and independence maximization"
    - "importance weighted adversarial nets for partial domain adaptation"

Connected through hyperedge HE-4 (induced by source paper "looking back at labels a class based domain adaptation technique"):
  with members:
    - "adversarial adaptation of scene graph models for understanding civic issues"
    - "beyond sharing weights for deep domain adaptation"
    - "multimodal differential network for visual question generation"
    - "differential attention for visual question answering"
    - "importance weighted adversarial nets for partial domain adaptation"
    - "partial adversarial domain adaptation"
    - "multi adversarial domain adaptation"
    - "learning semantic sentence embeddings using sequential pair wise discriminator"

Connected through hyperedge HE-5 (induced by source paper "unsupervised visual domain adaptation a deep max margin gaussian process approach"):
  with members:
    - "boosting domain adaptation by discovering latent domains"
    - "mind the class weight bias weighted maximum mean discrepancy for unsupervised domain adaptation"
    - "image to image translation for domain adaptation"
    - "maximum classifier discrepancy for unsupervised domain adaptation"
    - "few shot adversarial domain adaptation"
    - "unsupervised domain adaptation with similarity learning"
    - "adversarial discriminative domain adaptation"
    - "learning multiple visual domains with residual adapters"

Connected through hyperedge HE-6 (induced by source paper "domain invariant learning using adaptive filter decomposition"):
  with members:
    - "beyond sharing weights for deep domain adaptation"
    - "learning to adapt structured output space for semantic segmentation"
    - "image to image translation for domain adaptation"
    - "adversarial discriminative domain adaptation"
    - "asymmetric tri training for unsupervised domain adaptation"
    - "conditional adversarial domain adaptation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "deep transfer learning with joint adaptation networks"

Connected through hyperedge HE-7 (induced by source paper "disjoint label space transfer learning with common factorised space"):
  with members:
    - "multi task mid level feature alignment network for unsupervised cross dataset person re identification"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "lstd a low shot transfer detector for object detection"
    - "fine grained recognition in the wild a multi task domain adaptation approach"
    - "adversarial discriminative domain adaptation"
    - "learning multiple visual domains with residual adapters"
    - "asymmetric tri training for unsupervised domain adaptation"
    - "matching networks for one shot learning"

Connected through hyperedge HE-8 (induced by source paper "joint domain alignment and discriminative feature learning for unsupervised deep domain adaptation"):
  with members:
    - "beyond sharing weights for deep domain adaptation"
    - "parameter transfer extreme learning machine based on projective model"
    - "domain adaptation for visual applications a comprehensive survey"
    - "adversarial discriminative domain adaptation"
    - "deep transfer learning with joint adaptation networks"
    - "deep coral correlation alignment for deep domain adaptation"
    - "domain separation networks"
    - "return of frustratingly easy domain 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)