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: "neyman pearson detection of gauss markov signals in noise closed form error exponentand properties"
  Abstract: The performance of Neyman-Pearson detection of correlated random signals using noisy observations is considered. Using the large deviations principle, the performance is analyzed via the error exponent for the miss probability with a fixed false-alarm probability. Using the state-space structure of the signal and observation model, a closed-form expression for the error exponent is derived using the innovations approach, and the connection between the asymptotic behavior of the optimal detector and that of the Kalman filter is established. The properties of the error exponent are investigated for the scalar case. It is shown that the error exponent has distinct characteristics with respect to correlation strength: for signal-to-noise ratio (SNR) /spl ges/1, the error exponent is monotonically decreasing as the correlation becomes strong whereas for SNR<1 there is an optimal correlation that maximizes the error exponent for a given SNR.

Connected through hyperedge HE-1 (induced by source paper "limits of detecting text generated by large scale language models"):
  with members:
    - "the curious case of neural text degeneration"
    - "unifying human and statistical evaluation for natural language generation"
    - "human and automatic detection of generated text"
    - "real or fake learning to discriminate machine from human generated text"
    - "ctrl a conditional transformer language model for controllable generation"
    - "release strategies and the social impacts of language models"
    - "gltr statistical detection and visualization of generated text"
    - "defending against neural fake news"

Connected through hyperedge HE-2 (induced by source paper "optimal detection and error exponents for hidden multi state processes via random duration model approach"):
  with members:
    - "detecting random walks on graphs with heterogeneous sensors"
    - "optimal detection of random walks on graphs performance analysis via statistical physics"

Connected through hyperedge HE-3 (induced by source paper "detecting random walks on graphs with heterogeneous sensors"):
  with members:
    - "optimal detection of random walks on graphs performance analysis via statistical physics"

Connected through hyperedge HE-4 (induced by source paper "training beam sequence design for millimeter wave mimo systems a pomdp framework"):
  with members:
    - "pilot beam sequence design for channel estimation in millimeter wave mimo systems a pomdp framework"
    - "pilot signal design for massive mimo systems a received signal to noise ratio based approach"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"
    - "pilot beam pattern design for channel estimation in massive mimo systems"
    - "downlink training techniques for fdd massive mimo systems open loop and closed loop training with memory"
    - "millimeter wave mimo channel tracking systems"
    - "millimeter wave beamforming for wireless backhaul and access in small cell networks"
    - "incremental pruning a simple fast exact method for partially observable markov decision processes"

Connected through hyperedge HE-5 (induced by source paper "high rate vector quantization for the neyman pearson detection of correlated processes"):
  with members:
    - "distributed scalar quantization for computing high resolution analysis and extensions"
    - "error exponents for neyman pearson detection of a continuous time gaussian markov process from noisy irregular samples"

Connected through hyperedge HE-6 (induced by source paper "on minimax robust detection of stationary gaussian signals in white gaussian noise"):
  with members:
    - "orthogonal codes for robust low cost communication"

Connected through hyperedge HE-7 (induced by source paper "information energy and density for ad hoc sensor networks over correlated random fields large deviations analysis"):
  with members:
    - "how much information can one get from a wireless ad hoc sensor network over a correlated random field"
    - "large deviations analysis for the detection of 2d hidden gauss markov random fields using sensor networks"
    - "sensor configuration and activation for field detection in large sensor arrays"

Connected through hyperedge HE-8 (induced by source paper "large deviations analysis for the detection of 2d hidden gauss markov random fields using sensor networks"):
  with members:
    - "sensor configuration and activation for field detection in large sensor arrays"

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)