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: "ac dc in database learning thunderstruck"
  Abstract: We report on the design and implementation of the AC/DC gradient descent solver for a class of optimization problems over normalized databases. AC/DC decomposes an optimization problem into a set of aggregates over the join of the database relations. It then uses the answers to these aggregates to iteratively improve the solution to the problem until it converges. #R##N#The challenges faced by AC/DC are the large database size, the mixture of continuous and categorical features, and the large number of aggregates to compute. AC/DC addresses these challenges by employing a sparse data representation, factorized computation, problem reparameterization under functional dependencies, and a data structure that supports shared computation of aggregates. #R##N#To train polynomial regression models and factorization machines of up to 141K features over the join of a real-world dataset of up to 86M tuples, AC/DC needs up to 30 minutes on one core of a commodity machine. This is up to three orders of magnitude faster than its competitors R, MadLib, libFM, and TensorFlow whenever they finish and thus do not exceed memory limitation, 24-hour timeout, or internal design limitations.

Connected through hyperedge HE-1 (induced by source paper "multi layer optimizations for end to end data analytics"):
  with members:
    - "a layered aggregate engine for analytics workloads"
    - "efficient differentiable programming in a functional array processing language"
    - "stream fusion to completeness"
    - "building efficient query engines in a high level language"
    - "towards linear algebra over normalized data"
    - "xgboost a scalable tree boosting system"
    - "hpat high performance analytics with scripting ease of use"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-2 (induced by source paper "rk means fast clustering for relational data"):
  with members:
    - "strong coresets for k median and subspace approximation goodbye dimension"
    - "on functional aggregate queries with additive inequalities"
    - "coresets for k means and k median clustering and their applications"
    - "worst case optimal join algorithms techniques results and open problems"
    - "constraint solving via fractional edge covers"
    - "clustering high dimensional dynamic data streams"
    - "in database learning with sparse tensors"
    - "juggling functions inside a database"

Connected through hyperedge HE-3 (induced by source paper "a layered aggregate engine for analytics workloads"):
  with members:
    - "in database learning with sparse tensors"
    - "building efficient query engines in a high level language"
    - "towards linear algebra over normalized data"
    - "xgboost a scalable tree boosting system"
    - "tensorflow a system for large scale machine learning"
    - "the ctu prague relational learning repository"
    - "mllib machine learning in apache spark"
    - "faq questions asked frequently"

Connected through hyperedge HE-4 (induced by source paper "memory efficient group by aggregates over multi way joins"):
  with members:
    - "on functional aggregate queries with additive inequalities"
    - "covers of query results"
    - "boolean tensor decomposition for conjunctive queries with negation"
    - "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-5 (induced by source paper "sql4ml a declarative end to end workflow for machine learning"):
  with members:
    - "a layered aggregate engine for analytics workloads"
    - "declarative recursive computation on an rdbms or why you should use a database for distributed machine learning"
    - "on matrices and k relations"
    - "in database learning with sparse tensors"
    - "towards linear algebra over normalized data"
    - "tensorflow a system for large scale machine learning"
    - "the madlib analytics library or mad skills the sql"
    - "towards a unified architecture for in rdbms analytics"

Connected through hyperedge HE-6 (induced by source paper "mining approximate acyclic schemes from relations"):
  with members:
    - "data management for causal algorithmic fairness"
    - "a layered aggregate engine for analytics workloads"
    - "faq questions asked frequently"

Connected through hyperedge HE-7 (induced by source paper "worst case optimal join algorithms techniques results and open problems"):
  with members:
    - "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"
    - "what do shannon type inequalities submodular width and disjunctive datalog have to do with one another"
    - "computing join queries with functional dependencies"

Connected through hyperedge HE-8 (induced by source paper "on functional aggregate queries with additive inequalities"):
  with members:
    - "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"
    - "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)