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: "infrastructure quality assessment in africa using satellite imagery and deep learning"
  Abstract: The UN Sustainable Development Goals allude to the importance of infrastructure quality in three of its seventeen goals. However, monitoring infrastructure quality in developing regions remains prohibitively expensive and impedes efforts to measure progress toward these goals. To this end, we investigate the use of widely available remote sensing data for the prediction of infrastructure quality in Africa. We train a convolutional neural network to predict ground truth labels from the Afrobarometer Round 6 survey using Landsat 8 and Sentinel 1 satellite imagery. Our best models predict infrastructure quality with AUROC scores of 0.881 on Electricity, 0.862 on Sewerage, 0.739 on Piped Water, and 0.786 on Roads using Landsat 8. These performances are significantly better than models that leverage OpenStreetMap or nighttime light intensity on the same tasks. We also demonstrate that our trained model can accurately make predictions in an unseen country after fine-tuning on a small sample of images. Furthermore, the model can be deployed in regions with limited samples to predict infrastructure outcomes with higher performance than nearest neighbor spatial interpolation.

Connected through hyperedge HE-1 (induced by source paper "tackling climate change with machine learning"):
  with members:
    - "pvnet a lrcn architecture for spatio temporal photovoltaic powerforecasting from numerical weather prediction"
    - "truck traffic monitoring with satellite images"
    - "artificial intelligence for social good"
    - "efficient multi objective optimization through population based parallel surrogate search"
    - "agriculture commodity arrival prediction using remote sensing data insights and beyond"
    - "decentralized flood forecasting using deep neural networks"
    - "machine vision for natural gas methane emissions detection using an infrared camera"
    - "learning optimal solutions for extremely fast ac optimal power flow"

Connected through hyperedge HE-2 (induced by source paper "distributed generation of privacy preserving data with user customization"):
  with members:
    - "understanding compressive adversarial privacy"
    - "protecting sensitive attributes via generative adversarial networks"
    - "learning adversarially fair and transferable representations"
    - "learning controllable fair representations"
    - "learning to protect communications with adversarial neural cryptography"
    - "data decisions and theoretical implications when adversarially learning fair representations"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"
    - "context aware generative adversarial privacy"

Connected through hyperedge HE-3 (induced by source paper "deep probabilistic kernels for sample efficient learning"):
  with members:
    - "sampled softmax with random fourier features"
    - "a closer look at few shot classification"
    - "semi supervised deep kernel learning regression with unlabeled data by minimizing predictive variance"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "prototypical networks for few shot learning"
    - "learning to compare relation network for few shot learning"
    - "deep bayesian active learning with image data"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-4 (induced by source paper "predicting economic development using geolocated wikipedia articles"):
  with members:
    - "learning to interpret satellite images in global scale using wikipedia"
    - "learning to interpret satellite images using wikipedia"
    - "learning two branch neural networks for image text matching tasks"
    - "an empirical evaluation of doc2vec with practical insights into document embedding generation"
    - "breakingnews article annotation by image and text processing"
    - "text matching as image recognition"
    - "predicting deep zero shot convolutional neural networks using textual descriptions"
    - "transfer learning from deep features for remote sensing and poverty mapping"

Connected through hyperedge HE-5 (induced by source paper "apache spark accelerated deep learning inference for large scale satellite image analytics"):
  with members:
    - "building extraction at scale using convolutional neural network mapping of the united states"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "using convolutional networks and satellite imagery to identify patterns in urban environments at a large scale"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "tensorflow a system for large scale machine learning"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-6 (induced by source paper "semi supervised deep kernel learning regression with unlabeled data by minimizing predictive variance"):
  with members:
    - "adversarial constraint learning for structured prediction"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "learning scalable deep kernels with recurrent structure"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "kernel interpolation for scalable structured gaussian processes kiss gp"
    - "deep kernel learning"
    - "semi supervised learning with ladder networks"
    - "transfer learning from deep features for remote sensing and poverty mapping"

Connected through hyperedge HE-7 (induced by source paper "understanding compressive adversarial privacy"):
  with members:
    - "minimax filter learning to preserve privacy from inference attacks"
    - "controller synthesis for linear time varying systems with adversaries"
    - "sequence to sequence learning with neural networks"

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.CY(Computers and Society)