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: "deep transfer network unsupervised domain adaptation"
  Abstract: Domain adaptation aims at training a classifier in one dataset and applying it to a related but not identical dataset. One successfully used framework of domain adaptation is to learn a transformation to match both the distribution of the features (marginal distribution), and the distribution of the labels given features (conditional distribution). In this paper, we propose a new domain adaptation framework named Deep Transfer Network (DTN), where the highly flexible deep neural networks are used to implement such a distribution matching process. #R##N#This is achieved by two types of layers in DTN: the shared feature extraction layers which learn a shared feature subspace in which the marginal distributions of the source and the target samples are drawn close, and the discrimination layers which match conditional distributions by classifier transduction. We also show that DTN has a computation complexity linear to the number of training samples, making it suitable to large-scale problems. By combining the best paradigms in both worlds (deep neural networks in recognition, and matching marginal and conditional distributions in domain adaptation), we demonstrate by extensive experiments that DTN improves significantly over former methods in both execution time and classification accuracy.

Connected through hyperedge HE-1 (induced by source paper "multi source domain adaptation for text classification via distancenet bandits"):
  with members:
    - "beyond sharing weights for deep domain adaptation"
    - "autosem automatic task selection and mixing in multi task learning"
    - "a survey of domain adaptation for neural machine translation"
    - "deep visual domain adaptation a survey"
    - "multi source domain adaptation with mixture of experts"
    - "unsupervised domain adaptation based on source guided discrepancy"
    - "deep contextualized word representations"
    - "multi source cross lingual model transfer learning what to share"

Connected through hyperedge HE-2 (induced by source paper "domain adaptation with conditional distribution matching and generalized label shift"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "domain agnostic learning with disentangled representations"
    - "robust natural language inference models with example forgetting"
    - "batch weight for domain adaptation with mass shift"
    - "learning to transfer examples for partial domain adaptation"
    - "learning representations by maximizing mutual information across views"
    - "regularized learning for domain adaptation under label shifts"
    - "on learning invariant representation for domain adaptation"

Connected through hyperedge HE-3 (induced by source paper "adversarial camera alignment network for unsupervised cross camera person re identification"):
  with members:
    - "camera adversarial transfer for unsupervised person re identification"
    - "imitating targets from all sides an unsupervised transfer learning method for person re identification"
    - "unsupervised tracklet person re identification"
    - "a novel unsupervised camera aware domain adaptation framework for person re identification"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "multi task mid level feature alignment network for unsupervised cross dataset person re identification"
    - "re identification with consistent attentive siamese networks"
    - "unsupervised cross dataset person re identification by transfer learning of spatial temporal patterns"

Connected through hyperedge HE-4 (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"
    - "residual parameter transfer for deep domain adaptation"
    - "unsupervised domain adaptation with similarity learning"
    - "adversarial discriminative domain adaptation"

Connected through hyperedge HE-5 (induced by source paper "task discriminative domain alignment for unsupervised domain adaptation"):
  with members:
    - "boosting domain adaptation by discovering latent domains"
    - "mind the class weight bias weighted maximum mean discrepancy for unsupervised domain adaptation"
    - "maximum classifier discrepancy for unsupervised domain adaptation"
    - "good semi supervised learning that requires a bad gan"
    - "adversarial discriminative domain adaptation"
    - "asymmetric tri training for unsupervised domain adaptation"
    - "self ensembling for visual domain adaptation"
    - "minimal entropy correlation alignment for unsupervised deep domain adaptation"

Connected through hyperedge HE-6 (induced by source paper "a novel unsupervised camera aware domain adaptation framework for person re identification"):
  with members:
    - "unsupervised cross dataset person re identification by transfer learning of spatial temporal patterns"
    - "importance weighted adversarial nets for partial domain adaptation"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "domain adaptation through synthesis for unsupervised person re identification"
    - "human semantic parsing for person re identification"
    - "unsupervised person re identification by deep learning tracklet association"
    - "in defense of the triplet loss for person re identification"
    - "adversarial discriminative domain adaptation"

Connected through hyperedge HE-7 (induced by source paper "cross domain image matching in presence of outliers"):
  with members:
    - "cross domain image matching with deep feature maps"
    - "adversarially learned one class classifier for novelty detection"
    - "importance weighted adversarial nets for partial domain adaptation"
    - "anomaly detection using one class neural networks"
    - "cross view image matching for geo localization in urban environments"
    - "deep hashing network for unsupervised domain adaptation"
    - "adversarial discriminative domain adaptation"
    - "asymmetric tri training for unsupervised domain adaptation"

Connected through hyperedge HE-8 (induced by source paper "network together node classification via cross network deep network embedding"):
  with members:
    - "deep network embedding for graph representation learning in signed networks"
    - "adversarial training methods for network embedding"
    - "regal representation learning based graph alignment"
    - "struc2vec learning node representations from structural identity"
    - "adversarial network embedding"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "don t walk skip online learning of multi scale network embeddings"

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)