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: "learning from multiple outlooks"
  Abstract: We propose a novel problem formulation of learning a single task when the data are provided in different feature spaces. Each such space is called an outlook, and is assumed to contain both labeled and unlabeled data. The objective is to take advantage of the data from all the outlooks to better classify each of the outlooks. We devise an algorithm that computes optimal affine mappings from different outlooks to a target outlook by matching moments of the empirical distributions. We further derive a probabilistic interpretation of the resulting algorithm and a sample complexity bound indicating how many samples are needed to adequately find the mapping. We report the results of extensive experiments on activity recognition tasks that show the value of the proposed approach in boosting performance.

Connected through hyperedge HE-1 (induced by source paper "certified reinforcement learning with logic guidance"):
  with members:
    - "the probabilistic model checker storm"
    - "deepsynth program synthesis for automatic task segmentation in deep reinforcement learning"
    - "rethinking the discount factor in reinforcement learning a decision theoretic approach"
    - "verifiably safe off model reinforcement learning"
    - "logically constrained reinforcement learning"
    - "playing hard exploration games by watching youtube"
    - "from game theoretic multi agent log linear learning to reinforcement learning"
    - "observe and look further achieving consistent performance on atari"

Connected through hyperedge HE-2 (induced by source paper "jointly learning to label sentences and tokens"):
  with members:
    - "zero shot sequence labeling transferring knowledge from sentences to tokens"
    - "jfleg a fluency corpus and benchmark for grammatical error correction"
    - "on the state of the art of evaluation in neural language models"
    - "semi supervised multitask learning for sequence labeling"
    - "convolutional sequence to sequence learning"
    - "neural attention models for sequence classification analysis and application to key term extraction and dialogue act detection"
    - "compositional sequence labeling models for error detection in learner writing"
    - "neural machine translation with supervised attention"

Connected through hyperedge HE-3 (induced by source paper "a cross modal distillation network for person re identification in rgb depth"):
  with members:
    - "improving person re identification by attribute and identity learning"
    - "features for multi target multi camera tracking and re identification"
    - "deep visual domain adaptation a survey"
    - "adaptation and re identification network an unsupervised deep transfer learning approach to person re identification"
    - "in defense of the triplet loss for person re identification"
    - "unsupervised adaptive re identification in open world dynamic camera networks"
    - "learning deep context aware features over body and latent parts for person re identification"
    - "a comprehensive survey on cross modal retrieval"

Connected through hyperedge HE-4 (induced by source paper "domain adaptation for visual applications a comprehensive survey"):
  with members:
    - "beyond sharing weights for deep domain adaptation"
    - "generalized multi view embedding for visual recognition and cross modal retrieval"
    - "adversarial discriminative domain adaptation"
    - "unsupervised pixel level domain adaptation with generative adversarial networks"
    - "virtual worlds as proxy for multi object tracking analysis"
    - "play and learn using video games to train computer vision models"
    - "deep transfer learning with joint adaptation networks"
    - "lightweight unsupervised domain adaptation by convolutional filter reconstruction"

Connected through hyperedge HE-5 (induced by source paper "deep stacking networks for low resource chinese word segmentation with transfer learning"):
  with members:
    - "transfer deep learning for low resource chinese word segmentation with a novel neural network"
    - "deep multi task representation learning a tensor factorisation approach"
    - "neural word segmentation learning for chinese"
    - "adam a method for stochastic optimization"
    - "learning with augmented features for heterogeneous domain adaptation"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-6 (induced by source paper "learn on source refine on target a model transfer learning framework with random forests"):
  with members:
    - "recognizing activities and spatial context using wearable sensors"
    - "selective transfer learning for cross domain recommendation"
    - "frustratingly easy domain adaptation"
    - "domain adaptation for statistical classifiers"
    - "a model of inductive bias learning"

Connected through hyperedge HE-7 (induced by source paper "deep coral correlation alignment for deep domain adaptation"):
  with members:
    - "what do deep cnns learn about objects"
    - "return of frustratingly easy domain adaptation"
    - "simultaneous deep transfer across domains and tasks"
    - "learning transferable features with deep adaptation networks"
    - "deep domain confusion maximizing for domain invariance"
    - "caffe convolutional architecture for fast feature embedding"
    - "learning deep object detectors from 3d models"
    - "decaf a deep convolutional activation feature for generic visual recognition"

Connected through hyperedge HE-8 (induced by source paper "return of frustratingly easy domain adaptation"):
  with members:
    - "learning transferable features with deep adaptation networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "understanding deep image representations by inverting them"
    - "deep domain confusion maximizing for domain invariance"
    - "domain adaptive neural networks for object recognition"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "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)