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 functions learnability structure and optimization"
  Abstract: Submodular functions are discrete functions that model laws of diminishing returns and enjoy numerous algorithmic applications. They have been used in many areas, including combinatorial optimization, machine learning, and economics. In this work we study submodular functions from a learning theoretic angle. We provide algorithms for learning submodular functions, as well as lower bounds on their learnability. In doing so, we uncover several novel structural results revealing ways in which submodular functions can be both surprisingly structured and surprisingly unstructured. We provide several concrete implications of our work in other domains including algorithmic game theory and combinatorial optimization. #R##N#At a technical level, this research combines ideas from many areas, including learning theory (distributional learning and PAC-style analyses), combinatorics and optimization (matroids and submodular functions), and pseudorandomness (lossless expander graphs).

Connected through hyperedge HE-1 (induced by source paper "subadditive load balancing"):
  with members:
    - "mixed robust average submodular partitioning fast algorithms guarantees and applications"
    - "curvature and optimal algorithms for learning and minimizing submodular functions"
    - "optimal bounds on approximation of submodular and xos functions by juntas"
    - "optimal approximation for submodular and supermodular optimization with bounded curvature"
    - "learning with submodular functions a convex optimization perspective"
    - "efficient minimization of decomposable submodular functions"
    - "submodular approximation sampling based algorithms and lower bounds"

Connected through hyperedge HE-2 (induced by source paper "risk aware ranking for top k recommendations"):
  with members:
    - "saga a submodular greedy algorithm for group recommendation"
    - "empirical analysis of predictive algorithms for collaborative filtering"
    - "submodular approximation sampling based algorithms and lower bounds"

Connected through hyperedge HE-3 (induced by source paper "deep submodular functions"):
  with members:
    - "submodular optimization under noise"
    - "a new framework for distributed submodular maximization"
    - "towards minimizing k submodular functions"
    - "submodular function maximization via the multilinear relaxation and contention resolution schemes"
    - "efficient minimization of decomposable submodular functions"

Connected through hyperedge HE-4 (induced by source paper "tight bounds on low degree spectral concentration of submodular and xos functions"):
  with members:
    - "analysis of boolean functions"
    - "approximate resilience monotonicity and the complexity of agnostic learning"
    - "nearly tight bounds on ell_1 approximation of self bounding functions"
    - "optimal bounds on approximation of submodular and xos functions by juntas"
    - "representation approximation and learning of submodular functions using low rank decision trees"
    - "learning coverage functions and private release of marginals"
    - "on the sum of l1 influences"
    - "learning pseudo boolean k dnf and submodular functions"

Connected through hyperedge HE-5 (induced by source paper "nearly tight bounds on ell_1 approximation of self bounding functions"):
  with members:
    - "analysis of boolean functions"
    - "approximate resilience monotonicity and the complexity of agnostic learning"
    - "optimal bounds on approximation of submodular and xos functions by juntas"
    - "representation approximation and learning of submodular functions using low rank decision trees"
    - "learning coverage functions and private release of marginals"
    - "learning pseudo boolean k dnf and submodular functions"
    - "submodular functions are noise stable"
    - "learning valuation functions"

Connected through hyperedge HE-6 (induced by source paper "graph cuts with interacting edge costs examples approximations and algorithms"):
  with members:
    - "local distribution and the symmetry gap approximability of multiway partitioning problems"
    - "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"
    - "on triangulating dynamic graphical models"
    - "learning mixtures of submodular shells with application to document summarization"
    - "symmetry and approximability of submodular maximization problems"
    - "efficient minimization of decomposable submodular functions"

Connected through hyperedge HE-7 (induced by source paper "tight bounds on ell_1 approximation and learning of self bounding functions"):
  with members:
    - "tight bounds on low degree spectral concentration of submodular and xos functions"
    - "analysis of boolean functions"
    - "approximate resilience monotonicity and the complexity of agnostic learning"
    - "representation approximation and learning of submodular functions using low rank decision trees"
    - "learning coverage functions and private release of marginals"
    - "learning pseudo boolean k dnf and submodular functions"
    - "submodular functions are noise stable"
    - "learning valuation functions"

Connected through hyperedge HE-8 (induced by source paper "learning coverage functions and private release of marginals"):
  with members:
    - "agnostic learning of disjunctions on symmetric distributions"
    - "faster private release of marginals on small databases"
    - "submodular optimization with submodular cover and submodular knapsack constraints"
    - "optimal bounds on approximation of submodular and xos functions by juntas"
    - "efficient algorithms for privately releasing marginals via convex relaxations"
    - "representation approximation and learning of submodular functions using low rank decision trees"
    - "fingerprinting codes and the price of approximate differential privacy"
    - "testing coverage functions"

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.DS(Data Structures and Algorithms)