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: "cross modal common representation learning by hybrid transfer network"
  Abstract: DNN-based cross-modal retrieval is a research hotspot to retrieve across different modalities as image and text, but existing methods often face the challenge of insufficient cross-modal training data. In single-modal scenario, similar problem is usually relieved by transferring knowledge from large-scale auxiliary datasets (as ImageNet). Knowledge from such single-modal datasets is also very useful for cross-modal retrieval, which can provide rich general semantic information that can be shared across different modalities. However, it is challenging to transfer useful knowledge from single-modal (as image) source domain to cross-modal (as image/text) target domain. Knowledge in source domain cannot be directly transferred to both two different modalities in target domain, and the inherent cross-modal correlation contained in target domain provides key hints for cross-modal retrieval which should be preserved during transfer process. This paper proposes Cross-modal Hybrid Transfer Network (CHTN) with two subnetworks: Modal-sharing transfer subnetwork utilizes the modality in both source and target domains as a bridge, for transferring knowledge to both two modalities simultaneously; Layer-sharing correlation subnetwork preserves the inherent cross-modal semantic correlation to further adapt to cross-modal retrieval task. Cross-modal data can be converted to common representation by CHTN for retrieval, and comprehensive experiment on 3 datasets shows its effectiveness.

Connected through hyperedge HE-1 (induced by source paper "domain transfer for 3d pose estimation from color images without manual annotations"):
  with members:
    - "beyond sharing weights for deep domain adaptation"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "keep it unreal bridging the realism gap for 2 5d recognition with geometry priors only"
    - "unsupervised domain adaptation with adversarial residual transform networks"
    - "making deep heatmaps robust to partial occlusions for 3d object pose estimation"
    - "cross modal deep variational hand pose estimation"
    - "learning to estimate 3d hand pose from single rgb images"
    - "using a single rgb frame for real time 3d hand pose estimation in the wild"

Connected through hyperedge HE-2 (induced by source paper "data curation with deep learning vision towards self driving data curation"):
  with members:
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deep learning a critical appraisal"
    - "a survey of methods for explaining black box models"
    - "deeper deep entity resolution"
    - "mida multiple imputation using denoising autoencoders"
    - "unsupervised string transformation learning for entity consolidation"
    - "snorkel rapid training data creation with weak supervision"

Connected through hyperedge HE-3 (induced by source paper "data curation with deep learning vision"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "deeper deep entity resolution"
    - "snorkel rapid training data creation with weak supervision"
    - "holoclean holistic data repairs with probabilistic inference"
    - "a survey on network embedding"
    - "graph2vec learning distributed representations of graphs"

Connected through hyperedge HE-4 (induced by source paper "amalgamating knowledge towards comprehensive classification"):
  with members:
    - "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"
    - "like what you like knowledge distill via neuron selectivity transfer"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"
    - "deep residual learning for image recognition"
    - "cross modal distillation for supervision transfer"
    - "fitnets hints for thin deep nets"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "deep cross media knowledge transfer"):
  with members:
    - "an overview of cross media retrieval concepts methodologies benchmarks and challenges"
    - "transitive hashing network for heterogeneous multimedia retrieval"
    - "unsupervised domain adaptation with residual transfer networks"
    - "learning transferable features with deep adaptation networks"
    - "very deep convolutional networks for large scale image recognition"
    - "convolutional neural networks for sentence classification"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "mhtn modal adversarial hybrid transfer network for cross modal retrieval"):
  with members:
    - "deep cross media knowledge transfer"
    - "an overview of cross media retrieval concepts methodologies benchmarks and challenges"
    - "transitive hashing network for heterogeneous multimedia retrieval"
    - "learning aligned cross modal representations from weakly aligned data"
    - "unsupervised domain adaptation with residual transfer networks"
    - "learning transferable features with deep adaptation networks"
    - "cross modal distillation for supervision transfer"
    - "very deep convolutional networks for large scale image recognition"

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.MM(Multimedia)