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: "scalable probabilistic databases with factor graphs and mcmc"
  Abstract: Probabilistic databases play a crucial role in the management and understanding of uncertain data. However, incorporating probabilities into the semantics of incomplete databases has posed many challenges, forcing systems to sacrifice modeling power, scalability, or restrict the class of relational algebra formula under which they are closed. We propose an alternative approach where the underlying relational database always represents a single world, and an external factor graph encodes a distribution over possible worlds; Markov chain Monte Carlo (MCMC) inference is then used to recover this uncertainty to a desired level of fidelity. Our approach allows the efficient evaluation of arbitrary queries over probabilistic databases with arbitrary dependencies expressed by graphical models with structure that changes during inference. MCMC sampling provides efficiency by hypothesizing {\em modifications} to possible worlds rather than generating entire worlds from scratch. Queries are then run over the portions of the world that change, avoiding the onerous cost of running full queries over each sampled world. A significant innovation of this work is the connection between MCMC sampling and materialized view maintenance techniques: we find empirically that using view maintenance techniques is several orders of magnitude faster than naively querying each sampled world. We also demonstrate our system's ability to answer relational queries with aggregation, and demonstrate additional scalability through the use of parallelization.

Connected through hyperedge HE-1 (induced by source paper "joint reasoning for multi faceted commonsense knowledge"):
  with members:
    - "comet commonsense transformers for automatic knowledge graph construction"
    - "explain yourself leveraging language models for commonsense reasoning"
    - "embedding uncertain knowledge graphs"
    - "commonsenseqa a question answering challenge targeting commonsense knowledge"
    - "atomic an atlas of machine commonsense for if then reasoning"
    - "engaging image captioning via personality"
    - "moving beyond the turing test with the allen ai science challenge"
    - "vqa visual question answering"

Connected through hyperedge HE-2 (induced by source paper "in rdbms hardware acceleration of advanced analytics"):
  with members:
    - "distributed deep learning using synchronous stochastic gradient descent"
    - "eie efficient inference engine on compressed deep neural network"
    - "dimmwitted a study of main memory statistical analytics"
    - "shark sql and rich analytics at scale"
    - "towards a unified architecture for in rdbms analytics"
    - "optimal distributed online prediction using mini batches"

Connected through hyperedge HE-3 (induced by source paper "building a large scale multimodal knowledge base system for answering visual queries"):
  with members:
    - "don t just listen use your imagination leveraging visual common sense for non visual tasks"
    - "vqa visual question answering"
    - "ask your neurons a neural based approach to answering questions about images"
    - "are you talking to a machine dataset and methods for multilingual image question answering"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "imagenet large scale visual recognition challenge"
    - "dimmwitted a study of main memory statistical analytics"
    - "a multi world approach to question answering about real world scenes based on uncertain input"

Connected through hyperedge HE-4 (induced by source paper "incremental knowledge base construction using deepdive"):
  with members:
    - "from data fusion to knowledge fusion"
    - "logarithmic time updates and queries in probabilistic networks"
    - "feature engineering for knowledge base construction"
    - "errata distant supervision for relation extraction with matrix completion"
    - "linview incremental view maintenance for complex analytical queries"
    - "dimmwitted a study of main memory statistical analytics"
    - "a convolutional neural network for modelling sentences"
    - "adaptive inference on general graphical models"

Connected through hyperedge HE-5 (induced by source paper "asynchronous complex analytics in a distributed dataflow architecture"):
  with members:
    - "tupleware redefining modern analytics"
    - "dimmwitted a study of main memory statistical analytics"
    - "asynchronous distributed optimization using a randomized alternating direction method of multipliers"
    - "mli an api for distributed machine learning"
    - "the madlib analytics library or mad skills the sql"
    - "towards a unified architecture for in rdbms analytics"
    - "feature hashing for large scale multitask learning"

Connected through hyperedge HE-6 (induced by source paper "factorbase sql for learning a multi relational graphical model"):
  with members:
    - "a method for implementing a probabilistic model as a relational database"
    - "the madlib analytics library or mad skills the sql"
    - "towards a unified architecture for in rdbms analytics"
    - "lifted graphical models a survey"
    - "tuffy scaling up statistical inference in markov logic networks using an rdbms"

Connected through hyperedge HE-7 (induced by source paper "query driven sampling for collective entity resolution"):
  with members:
    - "probabilistic similarity logic"
    - "the madlib analytics library or mad skills the sql"
    - "cblock an automatic blocking mechanism for large scale de duplication tasks"
    - "query time entity resolution"

Connected through hyperedge HE-8 (induced by source paper "a multi world approach to question answering about real world scenes based on uncertain input"):
  with members:
    - "deep fragment embeddings for bidirectional image sentence mapping"
    - "a joint model of language and perception for grounded attribute learning"
    - "learning dependency based compositional semantics"

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)