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: "alternating projections for learning with expectation constraints"
  Abstract: We present an objective function for learning with unlabeled data that utilizes auxiliary expectation constraints. We optimize this objective function using a procedure that alternates between information and moment projections. Our method provides an alternate interpretation of the posterior regularization framework (Graca et al., 2008), maintains uncertainty during optimization unlike constraint-driven learning (Chang et al., 2007), and is more efficient than generalized expectation criteria (Mann & McCallum, 2008). Applications of this framework include minimally supervised learning, semisupervised learning, and learning with constraints that are more expressive than the underlying model. In experiments, we demonstrate comparable accuracy to generalized expectation criteria for minimally supervised learning, and use expressive structural constraints to guide semi-supervised learning, providing a 3%-6% improvement over stateof-the-art constraint-driven learning.

Connected through hyperedge HE-1 (induced by source paper "feature rich part of speech tagging for morphologically complex languages application to bulgarian"):
  with members:
    - "tnt a statistical part of speech tagger"

Connected through hyperedge HE-2 (induced by source paper "deep generative models with learnable knowledge constraints"):
  with members:
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "unsupervised text style transfer using language models as discriminators"
    - "texar a modularized versatile and extensible toolkit for text generation"
    - "learning to write with cooperative discriminators"
    - "unsupervised person image synthesis in arbitrary poses"
    - "training classifiers with natural language explanations"
    - "toward controlled generation of text"
    - "equivalence between policy gradients and soft q learning"

Connected through hyperedge HE-3 (induced by source paper "optimization with non differentiable constraints with applications to fairness recall churn and other goals"):
  with members:
    - "preventing fairness gerrymandering auditing and learning for subgroup fairness"
    - "robust optimization for non convex objectives"
    - "learning non discriminatory predictors"
    - "equality of opportunity in supervised learning"
    - "label free supervision of neural networks with physics and domain knowledge"
    - "satisfying real world goals with dataset constraints"
    - "a light touch for heavily constrained sgd"
    - "optimization learning and games with predictable sequences"

Connected through hyperedge HE-4 (induced by source paper "conditional random field autoencoders for unsupervised structured prediction"):
  with members:
    - "a fast and simple algorithm for training neural probabilistic language models"
    - "a universal part of speech tagset"
    - "unsupervised search based structured prediction"

Connected through hyperedge HE-5 (induced by source paper "cross lingual pseudo projected expectation regularization for weakly supervised learning"):
  with members:
    - "multilingual part of speech tagging two unsupervised approaches"

Connected through hyperedge HE-6 (induced by source paper "propto svm for learning with label proportions"):
  with members:
    - "learning about individuals from group statistics"

Connected through hyperedge HE-7 (induced by source paper "bayesian inference with posterior regularization and applications to infinite latent svms"):
  with members:
    - "a convex formulation for learning task relationships in multi task learning"
    - "bayesian learning in undirected graphical models approximate mcmc algorithms"
    - "bayesian random fields the bethe laplace approximation"
    - "on bregman distances and divergences of probability measures"

Connected through hyperedge HE-8 (induced by source paper "information theoretic semi supervised metric learning via entropy regularization"):
  with members:
    - "a survey on metric learning for feature vectors and structured data"
    - "robust metric learning by smooth optimization"

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)