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: "learning mixtures of submodular shells with application to document summarization"
  Abstract: We introduce a method to learn a mixture of submodular "shells" in a large-margin setting. A submodular shell is an abstract submodular function that can be instantiated with a ground set and a set of parameters to produce a submodular function. A mixture of such shells can then also be so instantiated to produce a more complex submodular function. What our algorithm learns are the mixture weights over such shells. We provide a risk bound guarantee when learning in a large-margin structured-prediction setting using a projected subgradient method when only approximate submodular optimization is possible (such as with submodular function maximization). We apply this method to the problem of multi-document summarization and produce the best results reported so far on the widely used NIST DUC-05 through DUC-07 document summarization corpora.

Connected through hyperedge HE-1 (induced by source paper "robust submodular minimization with applications to cooperative modeling"):
  with members:
    - "near optimal algorithms for hard submodular programs with discounted cooperative costs"
    - "approximating some network problems with scenarios"
    - "robust optimization for non convex objectives"
    - "robust submodular maximization offline and online algorithms"
    - "equilibrium computation and robust optimization in zero sum games with submodular structure"
    - "polyhedral aspects of submodularity convexity and concavity"
    - "submodular optimization with submodular cover and submodular knapsack constraints"
    - "curvature and optimal algorithms for learning and minimizing submodular functions"

Connected through hyperedge HE-2 (induced by source paper "diversity and inclusion metrics for subset selection"):
  with members:
    - "assessing and remedying coverage for a given dataset"
    - "the price of fair pca one extra dimension"
    - "ranking with fairness constraints"
    - "monte carlo markov chain algorithms for sampling strongly rayleigh distributions and determinantal point processes"
    - "measuring fairness in ranked outputs"
    - "how to be fair and diverse"
    - "fairness through awareness"
    - "solving the apparent diversity accuracy dilemma of recommender systems"

Connected through hyperedge HE-3 (induced by source paper "forming diverse teams from sequentially arriving people"):
  with members:
    - "an algorithm for multi attribute diverse matching"
    - "algorithms for optimal diverse matching"
    - "diverse weighted bipartite b matching"
    - "streaming non monotone submodular maximization personalized video summarization on the fly"
    - "ranking ideas for diversity and quality"
    - "fast algorithms for online stochastic convex programming"

Connected through hyperedge HE-4 (induced by source paper "an algorithm for multi attribute diverse matching"):
  with members:
    - "balancing relevance and diversity in online bipartite matching via submodularity"
    - "the conference paper assignment problem using order weighted averages to assign indivisible goods"
    - "the diverse cohort selection problem"

Connected through hyperedge HE-5 (induced by source paper "earlier isn t always better sub aspect analysis on corpus and system biases in summarization"):
  with members:
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "content selection in deep learning models of summarization"
    - "bottom up abstractive summarization"
    - "a simple theoretical model of importance for summarization"
    - "toward abstractive summarization using semantic representations"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "don t give me the details just the summary topic aware convolutional neural networks for extreme summarization"
    - "a dataset of peer reviews peerread collection insights and nlp applications"

Connected through hyperedge HE-6 (induced by source paper "unbiased evaluation of deep metric learning algorithms"):
  with members:
    - "ranked list loss for deep metric learning"
    - "few shot learning with embedded class models and shot free meta training"
    - "deep metric learning with bier boosting independent embeddings robustly"
    - "deep randomized ensembles for metric learning"
    - "deep metric learning with hierarchical triplet loss"
    - "attention based ensemble for deep metric learning"
    - "scalable logo recognition using proxies"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-7 (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-8 (induced by source paper "personalized bundle list recommendation"):
  with members:
    - "atrank an attention based user behavior modeling framework for recommendation"
    - "attention is all you need"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "six challenges for neural machine translation"
    - "sequence to sequence learning as beam search optimization"
    - "wide deep learning for recommender systems"
    - "context aware sequential recommendation"
    - "structured prediction energy 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.LG(Machine Learning)