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: "active anomaly detection in heterogeneous processes"
  Abstract: An active inference problem of detecting anomalies among heterogeneous processes is considered. At each time, a subset of processes can be probed. The objective is to design a sequential probing strategy that dynamically determines which processes to observe at each time and when to terminate the search so that the expected detection time is minimized under a constraint on the probability of misclassifying any process. This problem falls into the general setting of sequential design of experiments pioneered by Chernoff in 1959, in which a randomized strategy, referred to as the Chernoff test, was proposed and shown to be asymptotically optimal as the error probability approaches zero. For the problem considered in this paper, a low-complexity deterministic test is shown to enjoy the same asymptotic optimality while offering significantly better performance in the finite regime and faster convergence to the optimal rate function, especially when the number of processes is large. The computational complexity of the proposed test is also of a significantly lower order.

Connected through hyperedge HE-1 (induced by source paper "searching for anomalies over composite hypotheses"):
  with members:
    - "learning to detect an oddball target"
    - "optimal index policies for anomaly localization in resource constrained cyber systems"
    - "asymptotically optimal anomaly detection via sequential testing"
    - "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 "deep actor critic reinforcement learning for anomaly detection"):
  with members:
    - "statistical anomaly detection via composite hypothesis testing for markov models"
    - "robust sequential detection in distributed sensor networks"
    - "policy design for active sequential hypothesis testing using deep learning"

Connected through hyperedge HE-3 (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"
    - "wireless compressive sensing over fading channels with distributed sparse random projections"
    - "exploiting spatial correlation in energy constrained distributed detection"
    - "asymptotically optimal anomaly detection via sequential testing"

Connected through hyperedge HE-4 (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"
    - "proactive resource management in lte u systems a deep learning perspective"
    - "learning to communicate with deep multi agent reinforcement learning"
    - "deep reinforcement learning radio control and signal detection with kerlym a gym rl agent"

Connected through hyperedge HE-5 (induced by source paper "anomaly detection in hierarchical data streams under unknown models"):
  with members:
    - "asymptotically optimal anomaly detection via sequential testing"
    - "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"

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)