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: "asymptotically optimal anomaly detection via sequential testing"
  Abstract: Sequential detection of independent anomalous processes among K processes is considered. At each time, only M processes can be observed, and the observations from each chosen process follow two different distributions, depending on whether the process is normal or abnormal. Each anomalous process incurs a cost per unit time until its anomaly is identified and fixed. Switching across processes and state declarations are allowed at all times, while decisions are based on all past observations and actions. The objective is a sequential search strategy that minimizes the total expected cost incurred by all the processes during the detection process under reliability constraints. Low-complexity algorithms are established to achieve asymptotically optimal performance as the error constraints approach zero. Simulation results demonstrate strong performance in the finite regime.

Connected through hyperedge HE-1 (induced by source paper "searching for anomalies over composite hypotheses"):
  with members:
    - "active anomaly detection in heterogeneous processes"
    - "learning to detect an oddball target"
    - "optimal index policies for anomaly localization in resource constrained cyber systems"
    - "active sequential hypothesis testing"
    - "sequentiality and adaptivity gains in active hypothesis testing"
    - "controlled sensing for multihypothesis testing"
    - "sequential testing for sparse recovery"

Connected through hyperedge HE-2 (induced by source paper "spectrum and energy efficient multiple access for detection in wireless sensor networks"):
  with members:
    - "deep reinforcement one shot learning for artificially intelligent classification systems"
    - "active anomaly detection in heterogeneous processes"
    - "wireless compressive sensing over fading channels with distributed sparse random projections"
    - "exploiting spatial correlation in energy constrained distributed detection"

Connected through hyperedge HE-3 (induced by source paper "deep multi user reinforcement learning for distributed dynamic spectrum access"):
  with members:
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "approximate best response dynamics in random interference games"
    - "deep learning in mobile and wireless networking a survey"
    - "deep reinforcement one shot learning for artificially intelligent classification systems"
    - "deep reinforcement learning an overview"
    - "active anomaly detection in heterogeneous processes"
    - "proactive resource management in lte u systems a deep learning perspective"
    - "learning to communicate with deep multi agent reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "anomaly detection in hierarchical data streams under unknown models"):
  with members:
    - "active anomaly detection in heterogeneous processes"
    - "active sequential hypothesis testing"
    - "sequentiality and adaptivity gains in active hypothesis testing"
    - "controlled sensing for multihypothesis testing"
    - "non adaptive group testing explicit bounds and novel algorithms"
    - "boolean compressed sensing and noisy group testing"
    - "hierarchical heavy hitters with the space saving algorithm"
    - "graph constrained group testing"

Connected through hyperedge HE-5 (induced by source paper "utility maximizing sequential sensing over a finite horizon"):
  with members:
    - "reliability of sequential hypothesis testing can be achieved by an almost fixed length test"
    - "opportunistic advertisement scheduling in live social media a multiple stopping time pomdp approach"
    - "optimal index policies for anomaly localization in resource constrained cyber systems"
    - "censored truncated sequential spectrum sensing for cognitive radio networks"
    - "active sequential hypothesis testing"
    - "sequentiality and adaptivity gains in active hypothesis testing"
    - "controlled sensing for multihypothesis testing"
    - "eigenvalue based spectrum sensing algorithms for cognitive radio"

Connected through hyperedge HE-6 (induced by source paper "active anomaly detection in heterogeneous processes"):
  with members:
    - "utility maximizing sequential sensing over a finite horizon"
    - "learning to detect an oddball target"
    - "optimal index policies for anomaly localization in resource constrained cyber systems"
    - "active sequential hypothesis testing"
    - "sequentiality and adaptivity gains in active hypothesis testing"
    - "controlled sensing for multihypothesis testing"

Connected through hyperedge HE-7 (induced by source paper "asymptotically optimal stochastic encryption for quantized sequential detection in the presence of eavesdroppers"):
  with members:
    - "distributed bayesian detection in the presence of byzantine data"
    - "distributed inference in the presence of eavesdroppers a survey"

Connected through hyperedge HE-8 (induced by source paper "learning to detect an oddball target"):
  with members:
    - "neural dissimilarity indices that predict oddball detection in behaviour"
    - "universal outlier hypothesis testing"
    - "active sequential hypothesis testing"
    - "sequentiality and adaptivity gains in active hypothesis testing"
    - "controlled sensing for multihypothesis testing"

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)