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: "binary hypothesis testing game with training data"
  Abstract: We introduce a game-theoretic framework to study the hypothesis testing problem, in the presence of an adversary aiming at preventing a correct decision. Specifically, the paper considers a scenario in which an analyst has to decide whether a test sequence has been drawn according to a probability mass function (pmf) P_X or not. In turn, the goal of the adversary is to take a sequence generated according to a different pmf and modify it in such a way to induce a decision error. P_X is known only through one or more training sequences. We derive the asymptotic equilibrium of the game under the assumption that the analyst relies only on first order statistics of the test sequence, and compute the asymptotic payoff of the game when the length of the test sequence tends to infinity. We introduce the concept of indistinguishability region, as the set of pmf's that can not be distinguished reliably from P_X in the presence of attacks. Two different scenarios are considered: in the first one the analyst and the adversary share the same training sequence, in the second scenario, they rely on independent sequences. The obtained results are compared to a version of the game in which the pmf P_X is perfectly known to the analyst and the adversary.

Connected through hyperedge HE-1 (induced by source paper "cnn based adversarial embedding for image steganography"):
  with members:
    - "adversarial examples that fool both computer vision and time limited humans"
    - "large scale jpeg image steganalysis using hybrid deep learning framework"
    - "deep convolutional neural network to detect j uniward"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "adversarial examples in the physical world"
    - "the limitations of deep learning in adversarial settings"
    - "deepfool a simple and accurate method to fool deep neural networks"
    - "deep learning is a good steganalysis tool when embedding key is reused for different images even if there is a cover source mismatch"

Connected through hyperedge HE-2 (induced by source paper "nonzero sum adversarial hypothesis testing games"):
  with members:
    - "detection games under fully active adversaries"
    - "sok security and privacy in machine learning"
    - "strategic classification from revealed preferences"
    - "a game theoretic analysis of adversarial classification"
    - "adversarial hypothesis testing and a quantum stein s lemma for restricted measurements"
    - "stackelberg vs nash in security games an extended investigation of interchangeability equivalence and uniqueness"

Connected through hyperedge HE-3 (induced by source paper "cnn based steganalysis and parametric adversarial embedding a game theoretic framework"):
  with members:
    - "cnn based adversarial embedding for image steganography"
    - "adversarial source identification game with corrupted training"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "a game theoretic framework for optimum decision fusion in the presence of byzantines"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-4 (induced by source paper "detection games under fully active adversaries"):
  with members:
    - "adversarial source identification game with corrupted training"
    - "source distinguishability under distortion limited attack an optimal transport perspective"

Connected through hyperedge HE-5 (induced by source paper "adversarial source identification game with corrupted training"):
  with members:
    - "source distinguishability under distortion limited attack an optimal transport perspective"
    - "security evaluation of support vector machines in adversarial environments"

Connected through hyperedge HE-6 (induced by source paper "ddos attacks with randomized traffic innovation botnet identification challenges and strategies"):
  with members:
    - "distributed detection in tree networks byzantines and mitigation techniques"
    - "estimating traffic and anomaly maps via network tomography"
    - "the embedding capacity of information flows under renewal traffic"
    - "recovery of low rank plus compressed sparse matrices with application to unveiling traffic anomalies"
    - "dynamic anomalography tracking network anomalies via sparsity and low rank"
    - "mitigation of random query string dos via gossip"
    - "anonymous networking amidst eavesdroppers"

Connected through hyperedge HE-7 (induced by source paper "a game theoretic analysis of adversarial classification"):
  with members:
    - "a game theoretical approach for finding optimal strategies in an intruder classification game"
    - "stackelberg vs nash in security games an extended investigation of interchangeability equivalence and uniqueness"
    - "settling the complexity of computing two player nash equilibria"

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.IT(Information Theory)