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: "neural reverse engineering of stripped binaries"
  Abstract: We address the problem of reverse engineering of stripped executables which contain no debug information. This is a challenging problem because of the low amount of syntactic information available in stripped executables, and due to the diverse assembly code patterns arising from compiler optimizations. We present a novel approach for predicting procedure names in stripped executables. Our approach combines static analysis with encoder-decoder-based models. The main idea is to use static analysis to obtain enriched representations of API call sites; encode a set of sequences of these call sites by traversing the Control-Flow Graph; and finally, attend to the encoded sequences while decoding the target name. Our evaluation shows that our model performs predictions that are difficult and time consuming for humans, while improving on the state-of-the-art by $20\%$.

Connected through hyperedge HE-1 (induced by source paper "in nomine function naming functions in stripped binaries with neural networks"):
  with members:
    - "bertscore evaluating text generation with bert"
    - "dire a neural approach to decompiled identifier naming"
    - "tree2tree neural translation model for learning source code changes"
    - "universal language model fine tuning for text classification"
    - "a deep tree based model for software defect prediction"
    - "code2seq generating sequences from structured representations of code"
    - "attention is all you need"
    - "context2name a deep learning based approach to infer natural variable names from usage contexts"

Connected through hyperedge HE-2 (induced by source paper "dire a neural approach to decompiled identifier naming"):
  with members:
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "a general path based representation for predicting program properties"
    - "the adverse effects of code duplication in machine learning models of code"
    - "code2seq generating sequences from structured representations of code"
    - "context2name a deep learning based approach to infer natural variable names from usage contexts"
    - "neural message passing for quantum chemistry"
    - "a survey of machine learning for big code and naturalness"
    - "learning to represent programs with graphs"

Connected through hyperedge HE-3 (induced by source paper "adabot fault tolerant java decompiler"):
  with members:
    - "tranx a transition based neural abstract syntax parser for semantic parsing and code generation"
    - "attention is all you need"
    - "deep api learning"
    - "effective approaches to attention based neural machine translation"
    - "neural machine translation of rare words with subword units"
    - "on using very large target vocabulary for neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "function naming in stripped binaries using neural networks"):
  with members:
    - "dire a neural approach to decompiled identifier naming"
    - "towards neural decompilation"
    - "safe self attentive function embeddings for binary similarity"
    - "a survey of machine learning for big code and naturalness"
    - "distributed representations of sentences and documents"

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.LG(Machine Learning)