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: "causally regularized learning with agnostic data selection bias"
  Abstract: Most of previous machine learning algorithms are proposed based on the i.i.d. hypothesis. However, this ideal assumption is often violated in real applications, where selection bias may arise between training and testing process. Moreover, in many scenarios, the testing data is not even available during the training process, which makes the traditional methods like transfer learning infeasible due to their need on prior of test distribution. Therefore, how to address the agnostic selection bias for robust model learning is of paramount importance for both academic research and real applications. In this paper, under the assumption that causal relationships among variables are robust across domains, we incorporate causal technique into predictive modeling and propose a novel Causally Regularized Logistic Regression (CRLR) algorithm by jointly optimize global confounder balancing and weighted logistic regression. Global confounder balancing helps to identify causal features, whose causal effect on outcome are stable across domains, then performing logistic regression on those causal features constructs a robust predictive model against the agnostic bias. To validate the effectiveness of our CRLR algorithm, we conduct comprehensive experiments on both synthetic and real world datasets. Experimental results clearly demonstrate that our CRLR algorithm outperforms the state-of-the-art methods, and the interpretability of our method can be fully depicted by the feature visualization.

Connected through hyperedge HE-1 (induced by source paper "stable prediction with model misspecification and agnostic distribution shift"):
  with members:
    - "stable prediction across unknown environments"

Connected through hyperedge HE-2 (induced by source paper "a generalization theory based on independent and task identically distributed assumption"):
  with members:
    - "autoaugment learning augmentation policies from data"
    - "random erasing data augmentation"
    - "focal loss for dense object detection"
    - "mixup beyond empirical risk minimization"
    - "opening the black box of deep neural networks via information"
    - "a new distance measure for non identical data with application to image classification"
    - "understanding deep learning requires rethinking generalization"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "towards non i i d image classification a dataset and baselines"):
  with members:
    - "stable prediction across unknown environments"
    - "deep transfer learning with joint adaptation networks"
    - "yfcc100m the new data in multimedia research"
    - "simultaneous deep transfer across domains and tasks"
    - "learning transferable features with deep adaptation networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-4 (induced by source paper "distributionally robust multi instance learning with stable instances"):
  with members:
    - "attention based deep multiple instance learning"
    - "stable prediction across unknown environments"
    - "multi instance learning by treating instances as non i i d samples"

Connected through hyperedge HE-5 (induced by source paper "a survey of learning causality with data problems and methods"):
  with members:
    - "a fast pc algorithm for high dimensional causal discovery with multi core pcs"
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "stable prediction across unknown environments"
    - "linked causal variational autoencoder for inferring paired spillover effects"
    - "using simpson s paradox to discover interesting patterns in behavioral data"
    - "feature selection a data perspective"
    - "attributed network embedding for learning in a dynamic environment"
    - "cost optimal learning of causal graphs"

Connected through hyperedge HE-6 (induced by source paper "coco cn for cross lingual image tagging captioning and retrieval"):
  with members:
    - "self supervised video hashing with hierarchical binary auto encoder"
    - "predicting visual features from text for image and video caption retrieval"
    - "imagination improves multimodal translation"
    - "using artificial tokens to control languages for multilingual image caption generation"
    - "ai challenger a large scale dataset for going deeper in image understanding"
    - "an overview of cross media retrieval concepts methodologies benchmarks and challenges"
    - "image pivoting for learning multilingual multimodal representations"
    - "stair captions constructing a large scale japanese image caption dataset"

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)