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: "tiresias predicting security events through deep learning"
  Abstract: With the increased complexity of modern computer attacks, there is a need for defenders not only to detect malicious activity as it happens, but also to predict the specific steps that will be taken by an adversary when performing an attack. However this is still an open research problem, and previous research in predicting malicious events only looked at binary outcomes (eg. whether an attack would happen or not), but not at the specific steps that an attacker would undertake. To fill this gap we present Tiresias xspace, a system that leverages Recurrent Neural Networks (RNNs) to predict future events on a machine, based on previous observations. We test Tiresias xspace on a dataset of 3.4 billion security events collected from a commercial intrusion prevention system, and show that our approach is effective in predicting the next event that will occur on a machine with a precision of up to 0.93. We also show that the models learned by Tiresias xspace are reasonably stable over time, and provide a mechanism that can identify sudden drops in precision and trigger a retraining of the system. Finally, we show that the long-term memory typical of RNNs is key in performing event prediction, rendering simpler methods not up to the task.

Connected through hyperedge HE-1 (induced by source paper "hypersched dynamic resource reallocation for model development on a deadline"):
  with members:
    - "massively parallel hyperparameter tuning"
    - "an argument in favor of strong scaling for deep neural networks with small datasets"
    - "large batch size training of neural networks with adversarial training and second order information"
    - "tune a research platform for distributed model selection and training"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "slaq quality driven scheduling for distributed machine learning"
    - "deep reinforcement learning that matters"
    - "rllib abstractions for distributed reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "fortuneteller predicting microarchitectural attacks via unsupervised deep learning"):
  with members:
    - "fallout reading kernel writes from user space"
    - "zombieload cross privilege boundary data sampling"
    - "undermining user privacy on mobile devices using ai"
    - "ret2spec speculative execution using return stack buffers"
    - "microwalk a framework for finding side channels in binaries"
    - "state of the art speech recognition with sequence to sequence models"
    - "generic black box end to end attack against state of the art api call based malware classifiers"
    - "flush flush a fast and stealthy cache attack"

Connected through hyperedge HE-3 (induced by source paper "on the veracity of cyber intrusion alerts synthesized by generative adversarial networks"):
  with members:
    - "a style based generator architecture for generative adversarial networks"
    - "idsgan generative adversarial networks for attack generation against intrusion detection"
    - "voice impersonation using generative adversarial networks"
    - "mine mutual information neural estimation"
    - "black box attacks against rnn based malware detection algorithms"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "rnn based early cyber attack detection for the tennessee eastman process"

Connected through hyperedge HE-4 (induced by source paper "attack2vec leveraging temporal word embeddings to understand the evolution of cyberattacks"):
  with members:
    - "holmes real time apt detection through correlation of suspicious information flows"
    - "dynamic word embeddings for evolving semantic discovery"
    - "catching worms trojan horses and pups unsupervised detection of silent delivery campaigns"
    - "neural network based graph embedding for cross platform binary code similarity detection"
    - "tweet2vec character based distributed representations for social media"
    - "data poisoning attacks on factorization based collaborative filtering"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "towards safer smart contracts a sequence learning approach to detecting security threats"):
  with members:
    - "finding the greedy prodigal and suicidal contracts at scale"
    - "securify practical security analysis of smart contracts"
    - "attention is all you need"
    - "an empirical analysis of smart contracts platforms applications and design patterns"
    - "n gram opcode analysis for android malware detection"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "effective approaches to attention based neural machine translation"
    - "adam a method for stochastic optimization"

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)