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: "adversarial constraint learning for structured prediction"
  Abstract: Constraint-based learning reduces the burden of collecting labels by having users specify general properties of structured outputs, such as constraints imposed by physical laws. We propose a novel framework for simultaneously learning these constraints and using them for supervision, bypassing the difficulty of using domain expertise to manually specify constraints. Learning requires a black-box simulator of structured outputs, which generates valid labels, but need not model their corresponding inputs or the input-label relationship. At training time, we constrain the model to produce outputs that cannot be distinguished from simulated labels by adversarial training. Providing our framework with a small number of labeled inputs gives rise to a new semi-supervised structured prediction model; we evaluate this model on multiple tasks --- tracking, pose estimation and time series prediction --- and find that it achieves high accuracy with only a small number of labeled inputs. In some cases, no labels are required at all.

Connected through hyperedge HE-1 (induced by source paper "learning to interpret satellite images in global scale using wikipedia"):
  with members:
    - "predicting economic development using geolocated wikipedia articles"
    - "learning to interpret satellite images using wikipedia"
    - "tile2vec unsupervised representation learning for spatially distributed data"
    - "exploring the limits of weakly supervised pretraining"
    - "learning two branch neural networks for image text matching tasks"
    - "snorkel rapid training data creation with weak supervision"
    - "aerial vehicle tracking by adaptive fusion of hyperspectral likelihood maps"
    - "learning aerial image segmentation from online maps"

Connected through hyperedge HE-2 (induced by source paper "learning to interpret satellite images using wikipedia"):
  with members:
    - "tile2vec unsupervised representation learning for spatially distributed data"
    - "exploring the limits of weakly supervised pretraining"
    - "joint learning from earth observation and openstreetmap data to get faster better semantic maps"
    - "snorkel rapid training data creation with weak supervision"
    - "functional map of the world"
    - "yolo9000 better faster stronger"
    - "label free supervision of neural networks with physics and domain knowledge"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "semi supervised deep kernel learning regression with unlabeled data by minimizing predictive variance"):
  with members:
    - "infrastructure quality assessment in africa using satellite imagery and deep learning"
    - "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-4 (induced by source paper "streamlining variational inference for constraint satisfaction problems"):
  with members:
    - "the backtracking survey propagation algorithm for solving random k sat problems"
    - "label free supervision of neural networks with physics and domain knowledge"
    - "closing the gap between short and long xors for model counting"
    - "a new look at survey propagation and its generalizations"
    - "survey propagation an algorithm for satisfiability"

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)