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: "optimal aggregation algorithms for middleware"
  Abstract: Let D be a database of N objects where each object has m fields. The objects are given in m sorted lists (where the ith list is sorted according to the ith field). Our goal is to find the top k objects according to a monotone aggregation function t, while minimizing access to the lists. The problem arises in several contexts. In particular Fagin (JCSS 1999) considered it for the purpose of aggregating information in a multimedia database system. #R##N#We are interested in instance optimality, i.e. that our algorithm will be as good as any other (correct) algorithm on any instance. We provide and analyze several instance optimal algorithms for the task, with various access costs and models.

Connected through hyperedge HE-1 (induced by source paper "finding interesting frames in deep video analytics a top k approach"):
  with members:
    - "blazeit optimizing declarative aggregation and limit queries for neural network based video analytics"
    - "yolov3 an incremental improvement"
    - "focus querying large video datasets with low latency and low cost"
    - "scanner efficient video analysis at scale"
    - "occlusion aware hand pose estimation using hierarchical mixture density network"
    - "face detection using deep learning an improved faster rcnn approach"
    - "abnormality detection and localization in chest x rays using deep convolutional neural networks"
    - "noscope optimizing neural network queries over video at scale"

Connected through hyperedge HE-2 (induced by source paper "lsm based storage techniques a survey"):
  with members:
    - "efficient data ingestion and query processing for lsm based storage systems"
    - "asterixdb a scalable open source bdms"
    - "bigtable merge compaction"
    - "don t thrash how to cache your hash on flash"

Connected through hyperedge HE-3 (induced by source paper "can celebrities burst your bubble"):
  with members:
    - "mitigating confirmation bias on twitter by recommending opposing views"
    - "reducing controversy by connecting opposing views"
    - "big questions for social media big data representativeness validity and other methodological pitfalls"

Connected through hyperedge HE-4 (induced by source paper "look before you hop conversational question answering over knowledge graphs using judicious context expansion"):
  with members:
    - "answering complex questions by joining multi document evidence with quasi knowledge graphs"
    - "interpretation of natural language rules in conversational machine reading"
    - "comqa a community sourced dataset for complex factoid question answering with paraphrase clusters"
    - "the web as a knowledge base for answering complex questions"
    - "coqa a conversational question answering challenge"
    - "open domain question answering using early fusion of knowledge bases and text"
    - "complex sequential question answering towards learning to converse over linked question answer pairs with a knowledge graph"
    - "learning to paraphrase for question answering"

Connected through hyperedge HE-5 (induced by source paper "subjective databases"):
  with members:
    - "voyageur an experiential travel search engine"
    - "deep learning for sentiment analysis a survey"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "learning a neural semantic parser from user feedback"
    - "recursive neural conditional random fields for aspect based sentiment analysis"
    - "inducing domain specific sentiment lexicons from unlabeled corpora"

Connected through hyperedge HE-6 (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-7 (induced by source paper "constructive derandomization of query algorithms"):
  with members:
    - "lifting with simple gadgets and applications to circuit and proof complexity"
    - "beyond worst case analysis"
    - "query to communication lifting for bpp"
    - "the influence lower bound via query elimination"
    - "improved direct product theorems for randomized query complexity"
    - "optimal direct sum results for deterministic and randomized decision tree complexity"
    - "every decision tree has an influential variable"

Connected through hyperedge HE-8 (induced by source paper "semantic guided and response times bounded top k similarity search over knowledge graphs"):
  with members:
    - "accelerating partial evaluation in distributed sparql query evaluation"
    - "keyword search on rdf graphs a query graph assembly approach"
    - "an analytical study of large sparql query logs"
    - "kbqa learning question answering over qa corpora and knowledge bases"
    - "processing sparql queries over distributed rdf graphs"
    - "connecting language and knowledge bases with embedding models for relation extraction"
    - "querying knowledge graphs by example entity tuples"

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)