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 adaptation extreme learning machines for drift compensation in e nose systems"
  Abstract: This paper addresses an important issue known as sensor drift, which exhibits a nonlinear dynamic property in electronic nose (E-nose), from the viewpoint of machine learning. Traditional methods for drift compensation are laborious and costly owing to the frequent acquisition and labeling process for gas samples' recalibration. Extreme learning machines (ELMs) have been confirmed to be efficient and effective learning techniques for pattern recognition and regression. However, ELMs primarily focus on the supervised, semisupervised, and unsupervised learning problems in single domain (i.e., source domain). To our best knowledge, ELM with cross-domain learning capability has never been studied. This paper proposes a unified framework called domain adaptation extreme learning machine (DAELM), which learns a robust classifier by leveraging a limited number of labeled data from target domain for drift compensation as well as gas recognition in E-nose systems, without losing the computational efficiency and learning ability of traditional ELM. In the unified framework, two algorithms called source DAELM (DAELM-S) and target DAELM (DAELM-T) are proposed in this paper. In order to perceive the differences among ELM, DAELM-S, and DAELM-T, two remarks are provided. Experiments on the popular sensor drift data with multiple batches collected using E-nose system clearly demonstrate that the proposed DAELM significantly outperforms existing drift-compensation methods without cumbersome measures, and also bring new perspectives for ELM.

Connected through hyperedge HE-1 (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-2 (induced by source paper "adversarial transfer learning for cross domain visual recognition"):
  with members:
    - "when unsupervised domain adaptation meets tensor representations"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "generate to adapt aligning domains using generative adversarial networks"
    - "dualgan unsupervised dual learning for image to image translation"
    - "unsupervised pixel level domain adaptation with generative adversarial networks"
    - "improved techniques for training gans"
    - "deep transfer learning with joint adaptation networks"

Connected through hyperedge HE-3 (induced by source paper "deep nearest class mean model for incremental odor classification"):
  with members:
    - "constrained deep transfer feature learning and its applications"
    - "borrowing treasures from the wealthy deep transfer learning through selective joint fine tuning"
    - "a character level decoder without explicit segmentation for neural machine translation"
    - "joint training of a convolutional network and a graphical model for human pose estimation"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-4 (induced by source paper "parameter transfer extreme learning machine based on projective model"):
  with members:
    - "joint domain alignment and discriminative feature learning for unsupervised deep domain adaptation"
    - "return of frustratingly easy domain adaptation"
    - "efficient learning of domain invariant image representations"

Connected through hyperedge HE-5 (induced by source paper "svm and elm who wins object recognition with deep convolutional features from imagenet"):
  with members:
    - "naive deep face recognition touching the limit of lfw benchmark or not"
    - "evolutionary cost sensitive extreme learning machine and subspace extension"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"
    - "multi column deep neural networks for image classification"

Connected through hyperedge HE-6 (induced by source paper "robust visual knowledge transfer via eda"):
  with members:
    - "efficient learning of domain invariant image representations"
    - "learning with augmented features for heterogeneous domain adaptation"
    - "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.LG(Machine Learning)