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: "domain adaptive neural networks for object recognition"
  Abstract: We propose a simple neural network model to deal with the domain adaptation problem in object recognition. Our model incorporates the Maximum Mean Discrepancy (MMD) measure as a regularization in the supervised learning to reduce the distribution mismatch between the source and target domains in the latent space. From experiments, we demonstrate that the MMD regularization is an effective tool to provide good domain adaptation models on both SURF features and raw image pixels of a particular image data set. We also show that our proposed model, preceded by the denoising auto-encoder pretraining, achieves better performance than recent benchmark models on the same data sets. This work represents the first study of MMD measure in the context of neural networks.

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 "impact of imagenet model selection on domain adaptation"):
  with members:
    - "modified distribution alignment for domain adaptation with pre trained inception resnet"
    - "on minimum discrepancy estimation for deep domain adaptation"
    - "do better imagenet models transfer better"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "visual domain adaptation with manifold embedded distribution alignment"
    - "joint domain alignment and discriminative feature learning for unsupervised deep domain adaptation"
    - "automated identification of hookahs waterpipes on instagram an application in feature extraction using convolutional neural network and support vector machine classification"
    - "balanced distribution adaptation for transfer learning"

Connected through hyperedge HE-3 (induced by source paper "multi modal domain adaptation for fine grained action recognition"):
  with members:
    - "temporal attentive alignment for large scale video domain adaptation"
    - "stm spatiotemporal and motion encoding for action recognition"
    - "domain generalization by solving jigsaw puzzles"
    - "unsupervised domain adaptation through self supervision"
    - "adversarial cross domain action recognition with co attention"
    - "scaling egocentric vision the epic kitchens dataset"
    - "slowfast networks for video recognition"
    - "cooperative learning of audio and video models from self supervised synchronization"

Connected through hyperedge HE-4 (induced by source paper "class conditional alignment for partial domain adaptation"):
  with members:
    - "learning to transfer examples for partial domain adaptation"
    - "deep visual domain adaptation a survey"
    - "importance weighted adversarial nets for partial domain adaptation"
    - "partial adversarial domain adaptation"
    - "multi adversarial domain adaptation"
    - "mind the class weight bias weighted maximum mean discrepancy for unsupervised domain adaptation"
    - "autodial automatic domain alignment layers"
    - "deep hashing network for unsupervised domain adaptation"

Connected through hyperedge HE-5 (induced by source paper "partially shared variational auto encoders for unsupervised domain adaptation with target shift"):
  with members:
    - "m adda unsupervised domain adaptation with deep metric learning"
    - "learning to transfer examples for partial domain adaptation"
    - "cluster alignment with a teacher for unsupervised domain adaptation"
    - "a unified feature disentangler for multi domain image translation and manipulation"
    - "importance weighted adversarial nets for partial domain adaptation"
    - "partial adversarial domain adaptation"
    - "spectral normalization for generative adversarial networks"
    - "taking a closer look at domain shift category level adversaries for semantics consistent domain adaptation"

Connected through hyperedge HE-6 (induced by source paper "a comprehensive survey on transfer learning"):
  with members:
    - "contrastive adaptation network for unsupervised domain adaptation"
    - "transfer learning toolkit primers and benchmarks"
    - "domain agnostic learning with disentangled representations"
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "transfer learning with dynamic adversarial adaptation network"
    - "domain symmetric networks for adversarial domain adaptation"
    - "transferrable prototypical networks for unsupervised domain adaptation"
    - "deep visual domain adaptation a survey"

Connected through hyperedge HE-7 (induced by source paper "pixelvae improved pixelvae with discrete prior"):
  with members:
    - "implicit generation and generalization in energy based models"
    - "gumbolt extending gumbel trick to boltzmann priors"
    - "deep visual domain adaptation a survey"
    - "dvae discrete variational autoencoders with overlapping transformations"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "prototypical networks for few shot learning"
    - "fixing a broken elbo"
    - "vae with a vampprior"

Connected through hyperedge HE-8 (induced by source paper "reducing domain gap via style agnostic networks"):
  with members:
    - "domain generalization using a mixture of multiple latent domains"
    - "exploring the origins and prevalence of texture bias in convolutional neural networks"
    - "episodic training for domain generalization"
    - "srm a style based recalibration module for convolutional neural networks"
    - "domain generalization by solving jigsaw puzzles"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "semi supervised domain adaptation via minimax entropy"
    - "a closer look at few shot classification"

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)