human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "learning execution through neural code fusion"):
  Abstract: As the performance of computer systems stagnates due to the end of Moore's Law, there is a need for new models that can understand and optimize the execution of general purpose code. While there is a growing body of work on using Graph Neural Networks (GNNs) to learn representations of source code, these representations do not understand how code dynamically executes. In this work, we propose a new approach to use GNNs to learn fused representations of general source code and its execution. Our approach defines a multi-task GNN over low-level representations of source code and program state (i.e., assembly code and dynamic memory states), converting complex source code constructs and complex data structures into a simpler, more uniform format. We show that this leads to improved performance over similar methods that do not use execution and it opens the door to applying GNN models to new tasks that would not be feasible from static code alone. As an illustration of this, we apply the new model to challenging dynamic tasks (branch prediction and prefetching) from the SPEC CPU benchmark suite, outperforming the state-of-the-art by 26% and 45% respectively. Moreover, we use the learned fused graph embeddings to demonstrate transfer learning with high performance on an indirectly related task (algorithm classification).

Includes members:
  Member "a neural network prefetcher for arbitrary memory access patterns":
    also connected through other hyperedges:
      (none)
  Member "neural code comprehension a learnable representation of code semantics":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "what you see is what it means semantic representation learning of code based on visualization and transfer learning")
  Member "learning memory access patterns":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learned indexes for dynamic workloads")
  Member "path based function embedding and its application to specification mining":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a literature study of embeddings on source code")
  Member "code vectors understanding programs through embedded abstracted symbolic traces":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cc2vec distributed representations of code changes")
  Member "ithemal accurate portable and fast basic block throughput estimation using deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "automatic throughput and critical path analysis of x86 and arm assembly kernels")
  Member "neural arithmetic logic units":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "stealing black box functionality using the deep neural tree architecture")
  Member "neural guided deductive search for real time program synthesis from examples":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "comparison of syntactic and semantic representations of programs in neural embeddings")

Question: Please tell me which class the source paper 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.LG(Machine Learning)