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: "automated synthesis of semantic malware signatures using maximum satisfiability"
  Abstract: This paper proposes a technique for automatically learning semantic malware signatures for Android from very few samples of a malware family. The key idea underlying our technique is to look for a maximally suspicious common subgraph (MSCS) that is shared between all known instances of a malware family. An MSCS describes the shared functionality between multiple Android applications in terms of inter-component call relations and their semantic metadata (e.g., data-flow properties). Our approach identifies such maximally suspicious common subgraphs by reducing the problem to maximum satisfiability. Once a semantic signature is learned, our approach uses a combination of static analysis and a new approximate signature matching algorithm to determine whether an Android application matches the semantic signature characterizing a given malware family. #R##N#We have implemented our approach in a tool called ASTROID and show that it has a number of advantages over state-of-the-art malware detection techniques. First, we compare the semantic malware signatures automatically synthesized by ASTROID with manually-written signatures used in previous work and show that the signatures learned by ASTROID perform better in terms of accuracy as well as precision. Second, we compare ASTROID against two state-of-the-art malware detection tools and demonstrate its advantages in terms of interpretability and accuracy. Finally, we demonstrate that ASTROID's approximate signature matching algorithm is resistant to behavioral obfuscation and that it can be used to detect zero-day malware. In particular, we were able to find 22 instances of zero-day malware in Google Play that are not reported as malware by existing tools.

Connected through hyperedge HE-1 (induced by source paper "counterexample guided neural synthesis"):
  with members:
    - "neural program synthesis with priority queue training"
    - "leveraging grammar and reinforcement learning for neural program synthesis"
    - "robustfill neural program learning under noisy i o"
    - "programming not only by example"
    - "deepcoder learning to write programs"
    - "component based synthesis of table consolidation and transformation tasks from examples"
    - "neuro symbolic program synthesis"
    - "neural gpus learn algorithms"

Connected through hyperedge HE-2 (induced by source paper "new era of deeplearning based malware intrusion detection the malware detection and prediction based on deep learning"):
  with members:
    - "feudal networks for hierarchical reinforcement learning"
    - "improved training of wasserstein gans"
    - "long text generation via adversarial training with leaked information"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "mamadroid detecting android malware by building markov chains of behavioral models"
    - "xgboost a scalable tree boosting system"
    - "very deep convolutional networks for text classification"
    - "n opcode analysis for android malware classification and categorization"

Connected through hyperedge HE-3 (induced by source paper "approximation strategies for incomplete maxsat"):
  with members:
    - "generalized totalizer encoding for pseudo boolean constraints"
    - "incremental cardinality constraints for maxsat"
    - "solving linux upgradeability problems using boolean optimization"
    - "cause clue clauses error localization using maximum satisfiability"

Connected through hyperedge HE-4 (induced by source paper "mamadroid detecting android malware by building markov chains of behavioral models extended version"):
  with members:
    - "tesseract eliminating experimental bias in malware classification across space and time"
    - "a family of droids android malware detection via behavioral modeling static vs dynamic analysis"
    - "automated poisoning attacks and defenses in malware detection systems an adversarial machine learning approach"
    - "mamadroid detecting android malware by building markov chains of behavioral models"

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)