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: "what do shannon type inequalities submodular width and disjunctive datalog have to do with one another"
  Abstract: Recent works on bounding the output size of a conjunctive query with functional dependencies and degree constraints have shown a deep connection between fundamental questions in information theory and database theory. We prove analogous output bounds for disjunctive datalog rules, and answer several open questions regarding the tightness and looseness of these bounds along the way. Our bounds are intimately related to Shannon-type information inequalities. We devise the notion of a "proof sequence" of a specific class of Shannon-type information inequalities called "Shannon flow inequalities". We then show how such a proof sequence can be interpreted as symbolic instructions guiding an algorithm called "PANDA", which answers disjunctive datalog rules within the time that the size bound predicted. We show that PANDA can be used as a black-box to devise algorithms matching precisely the fractional hypertree width and the submodular width runtimes for aggregate and conjunctive queries with functional dependencies and degree constraints. #R##N#Our results improve upon known results in three ways. First, our bounds and algorithms are for the much more general class of disjunctive datalog rules, of which conjunctive queries are a special case. Second, the runtime of PANDA matches precisely the submodular width bound, while the previous algorithm by Marx has a runtime that is polynomial in this bound. Third, our bounds and algorithms work for queries with input cardinality bounds, functional dependencies, and degree constraints. #R##N#Overall, our results show a deep connection between three seemingly unrelated lines of research; and, our results on proof sequences for Shannon flow inequalities might be of independent interest.

Connected through hyperedge HE-1 (induced by source paper "constant delay enumeration with fpt preprocessing for conjunctive queries of bounded submodular width"):
  with members:
    - "enumeration on trees under relabelings"
    - "answering fo mod queries under updates on bounded degree databases"
    - "covers of query results"
    - "constraint solving via fractional edge covers"
    - "answering conjunctive queries under updates"
    - "compressed representations of conjunctive query results"
    - "answering ucqs under updates and in the presence of integrity constraints"
    - "tractable hypergraph properties for constraint satisfaction and conjunctive queries"

Connected through hyperedge HE-2 (induced by source paper "optimal algorithms for ranked enumeration of answers to full conjunctive queries"):
  with members:
    - "ranked enumeration of conjunctive query results"
    - "any k anytime top k tree pattern retrieval in labeled graphs"
    - "on functional aggregate queries with additive inequalities"
    - "on the enumeration complexity of unions of conjunctive queries"
    - "size bounds and query plans for relational joins"
    - "covers of query results"
    - "constraint solving via fractional edge covers"
    - "answering conjunctive queries under updates"

Connected through hyperedge HE-3 (induced by source paper "instance and output optimal parallel algorithms for acyclic joins"):
  with members:
    - "size bounds and query plans for relational joins"
    - "constraint solving via fractional edge covers"
    - "answering conjunctive queries under updates"
    - "worst case optimal algorithms for parallel query processing"
    - "it s all a matter of degree using degree information to optimize multiway joins"
    - "the input output complexity of triangle enumeration"
    - "skew in parallel query processing"
    - "gym a multiround join algorithm in mapreduce"

Connected through hyperedge HE-4 (induced by source paper "optimal joins using compact data structures"):
  with members:
    - "worst case optimal join algorithms techniques results and open problems"
    - "size bounds and query plans for relational joins"
    - "compressed representation of dynamic binary relations with applications"
    - "join processing for graph patterns an old dog with new tricks"
    - "faq questions asked frequently"
    - "joins via geometric resolutions worst case and beyond"
    - "faster compressed quadtrees"
    - "beyond worst case analysis for joins with minesweeper"

Connected through hyperedge HE-5 (induced by source paper "a join based hybrid parameter for constraint satisfaction"):
  with members:
    - "binary constraint satisfaction problems defined by excluded topological minors"
    - "size bounds and query plans for relational joins"
    - "constraint solving via fractional edge covers"
    - "general and fractional hypertree decompositions hard and easy cases"
    - "faq questions asked frequently"
    - "tractable hypergraph properties for constraint satisfaction and conjunctive queries"
    - "hypertree decompositions and tractable queries"
    - "using pivot consistency to decompose and solve functional csps"

Connected through hyperedge HE-6 (induced by source paper "bag query containment and information theory"):
  with members:
    - "size bounds and query plans for relational joins"
    - "constraint solving via fractional edge covers"
    - "computing join queries with functional dependencies"
    - "group characterizable entropy functions"

Connected through hyperedge HE-7 (induced by source paper "worst case optimal join algorithms techniques results and open problems"):
  with members:
    - "ac dc in database learning thunderstruck"
    - "distributed evaluation of subgraph queries using worstcase optimal lowmemory dataflows"
    - "size bounds and query plans for relational joins"
    - "constraint solving via fractional edge covers"
    - "in database learning with sparse tensors"
    - "juggling functions inside a database"
    - "worst case optimal algorithms for parallel query processing"
    - "computing join queries with functional dependencies"

Connected through hyperedge HE-8 (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"
    - "boolean tensor decomposition for conjunctive queries with negation"
    - "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)