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: "in rdbms hardware acceleration of advanced analytics"
  Abstract: The data revolution is fueled by advances in several areas, including databases, high-performance computer architecture, and machine learning. Although timely, there is a void of solutions that brings these disjoint directions together. This paper sets out to be the initial step towards such a union. The aim is to devise a solution for the in-Database Acceleration of Advanced Analytics (DAnA). DAnA empowers database users to leap beyond traditional data summarization techniques and seamlessly utilize hardware-accelerated machine learning. Deploying specialized hardware, such as FPGAs, for in-database analytics currently requires hand-designing the hardware and manually routing the data. Instead, DAnA automatically maps a high-level specification of in-database analytics queries to the FPGA accelerator. The accelerator implementation is generated from a User Defined Function (UDF), expressed as part of a SQL query in a Python-embedded Domain Specific Language (DSL). To realize efficient in-database integration, DAnA accelerators contain a novel hardware structure, Striders, that directly interface with the buffer pool of the database. DAnA obtains the schema and page layout information from the database catalog to configure the Striders. In turn, Striders extract, cleanse, and process the training data tuples, which are consumed by a multi-threaded FPGA engine that executes the analytics algorithm. We integrated DAnA with PostgreSQL to generate hardware accelerators for a range of real-world and synthetic datasets running diverse ML algorithms. Results show that DAnA-enhanced PostgreSQL provides, on average, 11.3x end-to-end speedup than MADLib and 5.4x faster than multi-threaded MADLib running on Greenplum. DAnA provides these benefits while hiding the complexity of hardware design from data scientists and allowing them to express the algorithm in 30-60 lines of Python.

Connected through hyperedge HE-1 (induced by source paper "accelerating generalized linear models with mlweaving a one size fits all system for any precision learning technical report"):
  with members:
    - "high accuracy low precision training"
    - "adaptive communication strategies to achieve the best error runtime trade off in local update sgd"
    - "bismo a scalable bit serial matrix multiplication overlay for reconfigurable computing"
    - "vibnn hardware acceleration of bayesian neural networks"
    - "e rnn design optimization for efficient recurrent neural networks in fpgas"
    - "neural cache bit serial in cache acceleration of deep neural networks"
    - "in datacenter performance analysis of a tensor processing unit"
    - "bridging the gap between neural networks and neuromorphic hardware with a neural network compiler"

Connected through hyperedge HE-2 (induced by source paper "polystore accelerated polystore system for heterogeneous workloads"):
  with members:
    - "relay a new ir for machine learning frameworks"
    - "rheemix in the data jungle a cross platform query optimizer"
    - "practical design space exploration"
    - "in datacenter performance analysis of a tensor processing unit"
    - "empowering in memory relational database engines with native graph processing"
    - "algorithmic performance accuracy trade off in 3d vision applications using hypermapper"
    - "pipegen data pipe generator for hybrid analytics"
    - "the bigdawg polystore system and architecture"

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)