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 pseudo boolean k dnf and submodular functions"
  Abstract: We prove that any submodular function f: {0,1}^n -> {0,1,...,k} can be represented as a pseudo-Boolean 2k-DNF formula. Pseudo-Boolean DNFs are a natural generalization of DNF representation for functions with integer range. Each term in such a formula has an associated integral constant. We show that an analog of Hastad's switching lemma holds for pseudo-Boolean k-DNFs if all constants associated with the terms of the formula are bounded. #R##N#This allows us to generalize Mansour's PAC-learning algorithm for k-DNFs to pseudo-Boolean k-DNFs, and hence gives a PAC-learning algorithm with membership queries under the uniform distribution for submodular functions of the form f:{0,1}^n -> {0,1,...,k}. Our algorithm runs in time polynomial in n, k^{O(k \log k / \epsilon)}, 1/\epsilon and log(1/\delta) and works even in the agnostic setting. The line of previous work on learning submodular functions [Balcan, Harvey (STOC '11), Gupta, Hardt, Roth, Ullman (STOC '11), Cheraghchi, Klivans, Kothari, Lee (SODA '12)] implies only n^{O(k)} query complexity for learning submodular functions in this setting, for fixed epsilon and delta. #R##N#Our learning algorithm implies a property tester for submodularity of functions f:{0,1}^n -> {0, ..., k} with query complexity polynomial in n for k=O((\log n/ \loglog n)^{1/2}) and constant proximity parameter \epsilon.

Connected through hyperedge HE-1 (induced by source paper "approximate mathbb f _2 sketching of valuation functions"):
  with members:
    - "optimality of linear sketching under modular updates"
    - "optimal lower bounds for universal relation and for samplers and finding duplicates in streams"
    - "testing submodularity and other properties of valuation functions"
    - "almost optimal streaming algorithms for coverage problems"
    - "tight bounds for single pass streaming complexity of the set cover problem"
    - "towards tight bounds for the streaming set cover problem"
    - "streaming algorithms for submodular function maximization"
    - "incidence geometries and the pass complexity of semi streaming set cover"

Connected through hyperedge HE-2 (induced by source paper "spectral sparsification of hypergraphs"):
  with members:
    - "cheeger inequalities for submodular transformations"
    - "sketching cuts in graphs and hypergraphs"
    - "hypergraph markov operators eigenvalues and approximation algorithms"
    - "representation approximation and learning of submodular functions using low rank decision trees"
    - "submodular functions are noise stable"
    - "fast approximation algorithms for cut based problems in undirected graphs"
    - "privately releasing conjunctions and the statistical query barrier"
    - "twice ramanujan sparsifiers"

Connected through hyperedge HE-3 (induced by source paper "learning combinatorial functions from pairwise comparisons"):
  with members:
    - "estimation from pairwise comparisons sharp minimax bounds with topology dependence"
    - "tight bounds on low degree spectral concentration of submodular and xos functions"
    - "curvature and optimal algorithms for learning and minimizing submodular functions"
    - "optimal bounds on approximation of submodular and xos functions by juntas"
    - "learning coverage functions and private release of marginals"
    - "active ranking using pairwise comparisons"
    - "learning valuation functions"
    - "privately releasing conjunctions and the statistical query barrier"

Connected through hyperedge HE-4 (induced by source paper "the limitations of optimization from samples"):
  with members:
    - "introduction to online convex optimization"
    - "online submodular welfare maximization greedy beats 1 2 in random order"
    - "submodular optimization under noise"
    - "robust influence maximization"
    - "the sample complexity of revenue maximization"
    - "the pseudo dimension of near optimal auctions"
    - "local distribution and the symmetry gap approximability of multiway partitioning problems"
    - "mixture selection mechanism design and signaling"

Connected through hyperedge HE-5 (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"
    - "submodular functions are noise stable"

Connected through hyperedge HE-6 (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"
    - "submodular functions are noise stable"
    - "learning valuation functions"
    - "a note on concentration of 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"
    - "submodular functions are noise stable"
    - "learning valuation functions"
    - "submodular functions learnability structure and optimization"

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.LG(Machine Learning)