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: "discovering archipelagos of tractability for constraint satisfaction and counting"
  Abstract: The Constraint Satisfaction Problem (CSP) is a central and generic computational problem which provides a common framework for many theoretical and practical applications. A central line of research is concerned with the identification of classes of instances for which CSP can be solved in polynomial time; such classes are often called "islands of tractability." A prominent way of defining islands of tractability for CSP is to restrict the relations that may occur in the constraints to a fixed set, called a constraint language, whereas a constraint language is conservative if it contains all unary relations. This paper addresses the general limit of the mentioned tractability results for CSP and #CSP, that they only apply to instances where all constraints belong to a single tractable language (in general, the union of two tractable languages isn't tractable). We show that we can overcome this limitation as long as we keep some control of how constraints over the various considered tractable languages interact with each other. For this purpose we utilize the notion of a \emph{strong backdoor} of a CSP instance, as introduced by Williams et al. (IJCAI 2003), which is a set of variables that when instantiated moves the instance to an island of tractability, i.e., to a tractable class of instances. In this paper, we consider strong backdoors into \emph{scattered classes}, consisting of CSP instances where each connected component belongs entirely to some class from a list of tractable classes. Our main result is an algorithm that, given a CSP instance with $n$ variables, finds in time $f(k)n^{O(1)}$ a strong backdoor into a scattered class (associated with a list of finite conservative constraint languages) of size $k$ or correctly decides that there isn't such a backdoor.

Connected through hyperedge HE-1 (induced by source paper "backdoors into heterogeneous classes of sat and csp"):
  with members:
    - "combining treewidth and backdoors for csp"
    - "solving problems on graphs of high rank width"
    - "meta kernelization using well structured modulators"
    - "on backdoors to tractable constraint languages"
    - "backdoors to abduction"
    - "strong backdoors to bounded treewidth sat"
    - "the tractability of csp classes defined by forbidden patterns"
    - "backdoors to satisfaction"

Connected through hyperedge HE-2 (induced by source paper "saving critical nodes with firefighters is fpt"):
  with members:
    - "a linear time parameterized algorithm for node unique label cover"
    - "a linear time parameterized algorithm for directed feedback vertex set"
    - "kernelization lower bounds by cross composition"

Connected through hyperedge HE-3 (induced by source paper "backdoors to tractable valued csp"):
  with members:
    - "the complexity landscape of decompositional parameters for ilp"
    - "differential game logic"
    - "necessary conditions for tractability of valued csps"
    - "on backdoors to tractable constraint languages"
    - "the complexity of conservative valued csps"
    - "backdoors to normality for disjunctive logic programs"
    - "backdoors to satisfaction"
    - "backdoors to tractable answer set programming"

Connected through hyperedge HE-4 (induced by source paper "combining treewidth and backdoors for csp"):
  with members:
    - "on backdoors to tractable constraint languages"
    - "designing fpt algorithms for cut problems using randomized contractions"
    - "strong backdoors to bounded treewidth sat"
    - "tractable hypergraph properties for constraint satisfaction and conjunctive queries"
    - "treewidth computation and extremal combinatorics"
    - "hypertree decompositions and tractable queries"

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)