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: "stable prediction across unknown environments"
  Abstract: In many important machine learning applications, the training distribution used to learn a probabilistic classifier differs from the testing distribution on which the classifier will be used to make predictions. Traditional methods correct the distribution shift by reweighting the training data with the ratio of the density between test and training data. In many applications training takes place without prior knowledge of the testing distribution on which the algorithm will be applied in the future. Recently, methods have been proposed to address the shift by learning causal structure, but those methods rely on the diversity of multiple training data to a good performance, and have complexity limitations in high dimensions. In this paper, we propose a novel Deep Global Balancing Regression (DGBR) algorithm to jointly optimize a deep auto-encoder model for feature selection and a global balancing model for stable prediction across unknown environments. The global balancing model constructs balancing weights that facilitate estimating of partial effects of features (holding fixed all other features), a problem that is challenging in high dimensions, and thus helps to identify stable, causal relationships between features and outcomes. The deep auto-encoder model is designed to reduce the dimensionality of the feature space, thus making global balancing easier. We show, both theoretically and with empirical experiments, that our algorithm can make stable predictions across unknown environments. Our experiments on both synthetic and real world datasets demonstrate that our DGBR algorithm outperforms the state-of-the-art methods for stable prediction across unknown environments.

Connected through hyperedge HE-1 (induced by source paper "stable prediction with model misspecification and agnostic distribution shift"):
  with members:
    - "causally regularized learning with agnostic data selection bias"

Connected through hyperedge HE-2 (induced by source paper "deep causal representation learning for unsupervised domain adaptation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "deep visual domain adaptation a survey"
    - "deep transfer learning with joint adaptation networks"
    - "fine to coarse knowledge transfer for low res image classification"
    - "deep coral correlation alignment for deep domain adaptation"
    - "unsupervised domain adaptation with residual transfer networks"
    - "binaryconnect training deep neural networks with binary weights during propagations"

Connected through hyperedge HE-3 (induced by source paper "ekt exercise aware knowledge tracing for student performance prediction"):
  with members:
    - "transcribing content from structural images with spotlight mechanism"
    - "a survey on network embedding"
    - "multi behavioral sequential prediction with recurrent log bilinear model"
    - "dynamic memory networks for visual and textual question answering"
    - "dynamic key value memory networks for knowledge tracing"
    - "end to end memory networks"
    - "lstm based deep learning models for non factoid answer selection"
    - "ask me anything dynamic memory networks for natural language processing"

Connected through hyperedge HE-4 (induced by source paper "towards non i i d image classification a dataset and baselines"):
  with members:
    - "causally regularized learning with agnostic data selection bias"
    - "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-5 (induced by source paper "adversarial balancing based representation learning for causal effect inference with observational data"):
  with members:
    - "adversarial balancing for causal inference"
    - "improved training of wasserstein gans"
    - "nips 2016 tutorial generative adversarial networks"
    - "tensorflow a system for large scale machine learning"
    - "doubly robust off policy value evaluation for reinforcement learning"
    - "counterfactual risk minimization learning from logged bandit feedback"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "distributionally robust multi instance learning with stable instances"):
  with members:
    - "attention based deep multiple instance learning"
    - "causally regularized learning with agnostic data selection bias"
    - "multi instance learning by treating instances as non i i d samples"

Connected through hyperedge HE-7 (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"
    - "linked causal variational autoencoder for inferring paired spillover effects"
    - "causally regularized learning with agnostic data selection bias"
    - "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"

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)