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: "a submodular supermodular procedure with applications to discriminative structure learning"
  Abstract: In this paper, we present an algorithm for minimizing the difference between two submodular functions using a variational framework which is based on (an extension of) the concave-convex procedure [17]. Because several commonly used metrics in machine learning, like mutual information and conditional mutual information, are submodular, the problem of minimizing the difference of two submodular problems arises naturally in many machine learning applications. Two such applications are learning discriminatively structured graphical models and feature selection under computational complexity constraints. A commonly used metric for measuring discriminative capacity is the EAR measure which is the difference between two conditional mutual information terms. Feature selection taking complexity considerations into account also fall into this framework because both the information that a set of features provide and the cost of computing and using the features can be modeled as submodular functions. This problem is NP-hard, and we give a polynomial time heuristic for it. We also present results on synthetic data to show that classifiers based on discriminative graphical models using this algorithm can significantly outperform classifiers based on generative graphical models.

Connected through hyperedge HE-1 (induced by source paper "a unified framework of robust submodular optimization"):
  with members:
    - "near optimal algorithms for hard submodular programs with discounted cooperative costs"
    - "learning from less data a unified data subset selection and active learning framework for computer vision"
    - "robust monotone submodular function maximization"
    - "distributionally robust submodular maximization"
    - "approximating some network problems with scenarios"
    - "a framework towards domain specific video summarization"
    - "robust optimization for non convex objectives"
    - "robust submodular maximization offline and online algorithms"

Connected through hyperedge HE-2 (induced by source paper "a memoization framework for scaling submodular optimization to large scale problems"):
  with members:
    - "stochastic submodular maximization the case of coverage functions"
    - "gradient methods for submodular maximization"
    - "deep submodular functions"
    - "submodular optimization under noise"
    - "noisy submodular maximization via adaptive sampling with applications to crowdsourced image collection summarization"
    - "provable submodular minimization using wolfe s algorithm"
    - "lazier than lazy greedy"
    - "submodular optimization with submodular cover and submodular knapsack constraints"

Connected through hyperedge HE-3 (induced by source paper "majorisation minimisation algorithms for minimising the difference between lattice submodular functions"):
  with members:
    - "continuous dr submodular maximization structure and algorithms"
    - "a reduction for optimizing lattice submodular functions with diminishing returns"
    - "submodular functions from discrete to continous domains"
    - "submodular function maximization on the bounded integer lattice"
    - "maximizing monotone submodular functions over the integer lattice"
    - "algorithms for approximate minimization of the difference between submodular functions with applications"
    - "learning mixtures of submodular shells with application to document summarization"
    - "maximizing sequence submodular functions and its application to online advertising"

Connected through hyperedge HE-4 (induced by source paper "clustering with fairness constraints a flexible and scalable approach"):
  with members:
    - "scalable fair clustering"
    - "fair and diverse dpp based data summarization"
    - "fair clustering through fairlets"
    - "the price of fair pca one extra dimension"
    - "fair coresets and streaming algorithms for fair k means clustering"
    - "scalable laplacian k modes"
    - "equality of opportunity in supervised learning"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-5 (induced by source paper "a submodularity based agglomerative clustering algorithm for the privacy funnel"):
  with members:
    - "generalizing bottleneck problems"
    - "from the information bottleneck to the privacy funnel"
    - "submodular optimization with submodular cover and submodular knapsack constraints"
    - "algorithms for approximate minimization of the difference between submodular functions with applications"
    - "privacy against statistical inference"
    - "convex analysis and optimization with submodular functions a tutorial"

Connected through hyperedge HE-6 (induced by source paper "batched stochastic bayesian optimization via combinatorial constraints design"):
  with members:
    - "algorithms for approximate minimization of the difference between submodular functions with applications"
    - "interactive submodular set cover"

Connected through hyperedge HE-7 (induced by source paper "non submodular function maximization subject to a matroid constraint with applications"):
  with members:
    - "submodular maximization beyond non negativity guarantees fast algorithms and applications"
    - "steering social activity a stochastic optimal control point of view"
    - "equity of attention amortizing individual fairness in rankings"
    - "deep reinforcement learning of marked temporal point processes"
    - "fairness of exposure in rankings"
    - "combinatorial penalties which structures are preserved by convex relaxations"
    - "weakly submodular maximization beyond cardinality constraints does randomization help greedy"
    - "gradient methods for submodular maximization"

Connected through hyperedge HE-8 (induced by source paper "deep clustering on the link between discriminative models and k means"):
  with members:
    - "clustering with deep learning taxonomy and new methods"
    - "deep clustering for unsupervised learning of visual features"
    - "deep clustering via joint convolutional autoencoder embedding and relative entropy minimization"
    - "training neural networks without gradients a scalable admm approach"
    - "joint unsupervised learning of deep representations and image clusters"
    - "unsupervised deep embedding for clustering analysis"
    - "volumetric bias in segmentation and reconstruction secrets and solutions"
    - "adam a method for stochastic 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)