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: "boolean tensor decomposition for conjunctive queries with negation"
  Abstract: We propose an algorithm for answering conjunctive queries with negation, where the negated relations are sparse. Its data complexity matches that of the best known algorithms for the positive subquery of the input query and is expressed in terms of the fractional hypertree width and the submodular width. The query complexity depends on the structure of the negated subquery; in general it is exponential in the number of join variables occurring in negated relations yet it becomes polynomial for several classes of queries. #R##N#This algorithm relies on several contributions. We show how to rewrite queries with negation on sparse relations into equivalent conjunctive queries with not-all-equal (NAE) predicates, which are a multi-dimensional analog of disequality. We then generalize the known color-coding technique to conjunctions of NAE predicates and explain it via a Boolean tensor decomposition of conjunctions of NAE predicates. This decomposition can be achieved via a probabilistic construction that can be derandomized efficiently.

Connected through hyperedge HE-1 (induced by source paper "learning models over relational data a brief tutorial"):
  with members:
    - "a layered aggregate engine for analytics workloads"
    - "incremental techniques for large scale dynamic query processing"
    - "counting triangles under updates in worst case optimal time"
    - "on functional aggregate queries with additive inequalities"
    - "size bounds and query plans for relational joins"
    - "incremental view maintenance with triple lock factorization benefits"
    - "constraint solving via fractional edge covers"
    - "building efficient query engines in a high level language"

Connected through hyperedge HE-2 (induced by source paper "memory efficient group by aggregates over multi way joins"):
  with members:
    - "ac dc in database learning thunderstruck"
    - "on functional aggregate queries with additive inequalities"
    - "covers of query results"
    - "worst case optimal algorithms for parallel query processing"
    - "aggregations over generalized hypertree decompositions"
    - "faq questions asked frequently"
    - "skew strikes back new developments in the theory of join algorithms"
    - "leapfrog triejoin a worst case optimal join algorithm"

Connected through hyperedge HE-3 (induced by source paper "on functional aggregate queries with additive inequalities"):
  with members:
    - "ac dc in database learning thunderstruck"
    - "worst case optimal join algorithms techniques results and open problems"
    - "learning models over relational data using sparse tensors and functional dependencies"
    - "constraint solving via fractional edge covers"
    - "in database learning with sparse tensors"
    - "juggling functions inside a database"
    - "what do shannon type inequalities submodular width and disjunctive datalog have to do with one another"
    - "faq questions asked frequently"

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.DB(Databases)