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: "submodular meets structured finding diverse subsets in exponentially large structured item sets"
  Abstract: To cope with the high level of ambiguity faced in domains such as Computer Vision or Natural Language processing, robust prediction methods often search for a diverse set of high-quality candidate solutions or proposals. In structured prediction problems, this becomes a daunting task, as the solution space (image labelings, sentence parses, etc.) is exponentially large. We study greedy algorithms for finding a diverse subset of solutions in structured-output spaces by drawing new connections between submodular functions over combinatorial item sets and High-Order Potentials (HOPs) studied for graphical models. Specifically, we show via examples that when marginal gains of submodular diversity functions allow structured representations, this enables efficient (sub-linear time) approximate maximization by reducing the greedy augmentation step to inference in a factor graph with appropriately constructed HOPs. We discuss benefits, tradeoffs, and show that our constructions lead to significantly better proposals.

Connected through hyperedge HE-1 (induced by source paper "diversity in machine learning"):
  with members:
    - "fast determinantal point processes via distortion free intermediate sampling"
    - "tagging like humans diverse and distinct image annotation"
    - "improving sequential determinantal point processes for supervised video summarization"
    - "diversity promoting bayesian learning of latent variable models"
    - "learning detection with diverse proposals"
    - "diversified texture synthesis with feed forward networks"
    - "stochastic multiple choice learning for training diverse deep ensembles"
    - "an ensemble diversity approach to supervised binary hashing"

Connected through hyperedge HE-2 (induced by source paper "beyond submodular maximization"):
  with members:
    - "distributed maximization of submodular plus diversity functions for multi label feature selection on huge datasets"
    - "weakly submodular maximization beyond cardinality constraints does randomization help greedy"
    - "online submodular welfare maximization greedy beats 1 2 in random order"
    - "guarantees for greedy maximization of non submodular functions with applications"
    - "local search for max sum diversification"
    - "max sum diversification monotone submodular functions and semi metric spaces"
    - "max sum diversity via convex programming"
    - "building a good team secretary problems and the supermodular degree"

Connected through hyperedge HE-3 (induced by source paper "monotone submodular diversity functions for categorical vectors with application to diversification of seeds for targeted influence maximization"):
  with members:
    - "community interaction and conflict on the web"
    - "topology driven diversity for targeted influence maximization with application to user engagement in social networks"
    - "why approximate when you can get the exact optimal targeted viral marketing at scale"
    - "from competition to complementarity comparative influence diffusion and maximization"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "maximizing social influence in nearly optimal time"

Connected through hyperedge HE-4 (induced by source paper "learning from less data diversified subset selection and active learning in image classification tasks"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "yolo9000 better faster stronger"
    - "matching networks for one shot learning"
    - "deepid3 face recognition with very deep neural networks"
    - "active transfer learning with zero shot priors reusing past datasets for future tasks"
    - "learning transferable features with deep adaptation networks"
    - "spatial transformer networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "melding the data decisions pipeline decision focused learning for combinatorial optimization"):
  with members:
    - "learning approximate inference networks for structured prediction"
    - "optnet differentiable optimization as a layer in neural networks"
    - "task based end to end model learning in stochastic optimization"
    - "learning combinatorial optimization algorithms over graphs"
    - "gradient methods for submodular maximization"
    - "guaranteed non convex optimization submodular maximization over continuous domains"
    - "on differentiating parameterized argmin and argmax problems with application to bi level optimization"
    - "the offset tree for learning with partial labels"

Connected through hyperedge HE-6 (induced by source paper "data distillation for controlling specificity in dialogue generation"):
  with members:
    - "deep reinforcement learning for dialogue generation"
    - "sequence to backward and forward sequences a content introducing approach to generative short text conversation"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "adversarial evaluation of dialogue models"
    - "on the evaluation of dialogue systems with next utterance classification"
    - "piecewise latent variables for neural variational text processing"
    - "coherent dialogue with attention based language models"
    - "online sequence to sequence active learning for open domain dialogue generation"

Connected through hyperedge HE-7 (induced by source paper "diverse m best solutions by dynamic programming"):
  with members:
    - "an efficient message passing algorithm for the m best map problem"

Connected through hyperedge HE-8 (induced by source paper "subquadratic submodular function minimization"):
  with members:
    - "random coordinate descent methods for minimizing decomposable submodular functions"
    - "a faster cutting plane method and its implications for combinatorial and convex optimization"
    - "tight bounds for approximate carath eodory and beyond"
    - "provable submodular minimization using wolfe s algorithm"
    - "submodular optimization with submodular cover and submodular knapsack constraints"
    - "curvature and optimal algorithms for learning and minimizing submodular functions"
    - "reflection methods for user friendly submodular optimization"
    - "learning with submodular functions a convex optimization perspective"

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)