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: "algorithms for implicit hitting set problems"
  Abstract: A hitting set for a collection of sets is a set that has a non-empty intersection with each set in the collection; the hitting set problem is to find a hitting set of minimum cardinality. Motivated by instances of the hitting set problem where the number of sets to be hit is large, we introduce the notion of implicit hitting set problems. In an implicit hitting set problem the collection of sets to be hit is typically too large to list explicitly; instead, an oracle is provided which, given a set H, either determines that H is a hitting set or returns a set that H does not hit. We show a number of examples of classic implicit hitting set problems, and give a generic algorithm for solving such problems optimally. The main contribution of this paper is to show that this framework is valuable in developing approximation algorithms. We illustrate this methodology by presenting a simple on-line algorithm for the minimum feedback vertex set problem on random graphs. In particular our algorithm gives a feedback vertex set of size n-(1/p)\log{np}(1-o(1)) with probability at least 3/4 for the random graph G_{n,p} (the smallest feedback vertex set is of size n-(2/p)\log{np}(1+o(1))). We also consider a planted model for the feedback vertex set in directed random graphs. Here we show that a hitting set for a polynomial-sized subset of cycles is a hitting set for the planted random graph and this allows us to exactly recover the planted feedback vertex set.

Connected through hyperedge HE-1 (induced by source paper "on validating repairing and refining heuristic ml explanations"):
  with members:
    - "a symbolic approach to explaining bayesian network classifiers"
    - "methods for interpreting and understanding deep neural networks"
    - "reachability analysis of deep neural networks with provable guarantees"
    - "sanity checks for saliency maps"
    - "abduction based explanations for machine learning models"
    - "deep learning for case based reasoning through prototypes a neural network that explains its predictions"
    - "improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients"
    - "a unified approach to interpreting model predictions"

Connected through hyperedge HE-2 (induced by source paper "graph theoretic framework for unified analysis of observability and data injection attacks in the smart grid"):
  with members:
    - "game theory for secure critical interdependent gas power water infrastructure"
    - "prospect theory for enhanced smart grid resilience using distributed energy storage"
    - "on bounded rationality in cyber physical systems security game theoretic analysis with application to smart grid protection"
    - "data injection attacks on smart grids with multiple adversaries a game theoretic perspective"
    - "computing critical k tuples in power networks"

Connected through hyperedge HE-3 (induced by source paper "model based diagnosis of multiple observations with implicit hitting sets"):
  with members:
    - "propositional abduction with implicit hitting sets"
    - "detecting inconsistencies in large biological networks with answer set programming"
    - "cause clue clauses error localization using maximum satisfiability"
    - "answer sets for consistent query answering in inconsistent databases"
    - "model based diagnosis using structured system descriptions"

Connected through hyperedge HE-4 (induced by source paper "propositional abduction with implicit hitting sets"):
  with members:
    - "efficient mus enumeration of horn formulae with applications to axiom pinpointing"
    - "an abductive framework for computing knowledge base updates"

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)