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 network based graph embedding for cross platform binary code similarity detection"
  Abstract: The problem of cross-platform binary code similarity detection aims at detecting whether two binary functions coming from different platforms are similar or not. It has many security applications, including plagiarism detection, malware detection, vulnerability search, etc. Existing approaches rely on approximate graph-matching algorithms, which are inevitably slow and sometimes inaccurate, and hard to adapt to a new task. To address these issues, in this work, we propose a novel neural network-based approach to compute the embedding, i.e., a numeric vector, based on the control flow graph of each binary function, then the similarity detection can be done efficiently by measuring the distance between the embeddings for two functions. We implement a prototype called Gemini. Our extensive evaluation shows that Gemini outperforms the state-of-the-art approaches by large margins with respect to similarity detection accuracy. Further, Gemini can speed up prior art's embedding generation time by 3 to 4 orders of magnitude and reduce the required training time from more than 1 week down to 30 minutes to 10 hours. Our real world case studies demonstrate that Gemini can identify significantly more vulnerable firmware images than the state-of-the-art, i.e., Genius. Our research showcases a successful application of deep learning on computer security problems.

Connected through hyperedge HE-1 (induced by source paper "equivalence of dataflow graphs via rewrite rules using a graph to sequence neural model"):
  with members:
    - "dire a neural approach to decompiled identifier naming"
    - "sequencer sequence to sequence learning for end to end program repair"
    - "a comprehensive survey on graph neural networks"
    - "code2vec learning distributed representations of code"
    - "an empirical study on learning bug fixing patches in the wild via neural machine translation"
    - "an empirical evaluation of rule extraction from recurrent neural networks"
    - "opennmt open source toolkit for neural machine translation"
    - "context2name a deep learning based approach to infer natural variable names from usage contexts"

Connected through hyperedge HE-2 (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"
    - "neural reverse engineering of stripped binaries"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "towards learning representations of binary executable files for security tasks"):
  with members:
    - "graph matching networks for learning the similarity of graph structured objects"
    - "neural code comprehension a learnable representation of code semantics"
    - "vuldeepecker a deep learning based system for vulnerability detection"
    - "neural machine translation inspired binary code similarity comparison beyond function pairs"
    - "robust and effective malware detection through quantitative data flow graph metrics"
    - "on the naturalness of buggy code"

Connected through hyperedge HE-4 (induced by source paper "deep graph similarity learning a survey"):
  with members:
    - "simgnn a neural network approach to fast graph similarity computation"
    - "deep graph matching consensus"
    - "dynamic graph convolutional networks"
    - "glmnet graph learning matching networks for feature matching"
    - "from community to role based graph embeddings"
    - "learning combinatorial embedding networks for deep graph matching"
    - "community preserving graph convolutions for structural and functional joint embedding of brain networks"
    - "generalizing from a few examples a survey on few shot learning"

Connected through hyperedge HE-5 (induced by source paper "graph matching networks for learning the similarity of graph structured objects"):
  with members:
    - "a survey on graph kernels"
    - "ddgk learning graph representations for deep divergence graph kernels"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "deep sets"
    - "learning deep generative models of graphs"
    - "relational inductive biases deep learning and graph networks"
    - "dynamic graph cnn for learning on point clouds"
    - "how powerful are graph neural networks"

Connected through hyperedge HE-6 (induced by source paper "a literature study of embeddings on source code"):
  with members:
    - "import2vec learning embeddings for software libraries"
    - "sorting and transforming program repair ingredients via deep learning code similarities"
    - "semantic embeddings for program behavior patterns"
    - "code2vec learning distributed representations of code"
    - "the remarkable role of similarity in redundancy based program repair"
    - "learning to represent edits"
    - "a cross architecture instruction embedding model for natural language processing inspired binary code analysis"
    - "path based function embedding and its application to specification mining"

Connected through hyperedge HE-7 (induced by source paper "evaluating explanation methods for deep learning in security"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "vuldeepecker a deep learning based system for vulnerability detection"
    - "interpretable deep learning under fire"
    - "sok security and privacy in machine learning"
    - "axiomatic attribution for deep networks"
    - "a unified approach to interpreting model predictions"
    - "learning important features through propagating activation differences"
    - "towards better understanding of gradient based attribution methods for deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "evaluating semantic representations of source code"):
  with members:
    - "coset a benchmark for evaluating neural program embeddings"
    - "a literature study of embeddings on source code"
    - "code2vec learning distributed representations of code"
    - "a general path based representation for predicting program properties"
    - "path based function embedding and its application to specification mining"
    - "code vectors understanding programs through embedded abstracted symbolic traces"
    - "automated software vulnerability detection with machine learning"
    - "code2seq generating sequences from structured representations of code"

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.CR(Cryptography and Security)