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: "sequentiality and adaptivity gains in active hypothesis testing"
  Abstract: Consider a decision maker who is responsible to collect observations so as to enhance his information in a speedy manner about an underlying phenomena of interest. The policies under which the decision maker selects sensing actions can be categorized based on the following two factors: i) sequential versus non-sequential; ii) adaptive versus non-adaptive. Non-sequential policies collect a fixed number of observation samples and make the final decision afterwards; while under sequential policies, the sample size is not known initially and is determined by the observation outcomes. Under adaptive policies, the decision maker relies on the previous collected samples to select the next sensing action; while under non-adaptive policies, the actions are selected independent of the past observation outcomes. In this paper, performance bounds are provided for the policies in each category. Using these bounds, sequentiality gain and adaptivity gain, i.e., the gains of sequential and adaptive selection of actions are characterized.

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"
    - "asymptotically optimal anomaly detection via sequential testing"
    - "active sequential hypothesis testing"
    - "controlled sensing for multihypothesis testing"
    - "sequential testing for sparse recovery"

Connected through hyperedge HE-2 (induced by source paper "anomaly detection in hierarchical data streams under unknown models"):
  with members:
    - "active anomaly detection in heterogeneous processes"
    - "asymptotically optimal anomaly detection via sequential testing"
    - "active sequential 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-3 (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"
    - "asymptotically optimal anomaly detection via sequential testing"
    - "censored truncated sequential spectrum sensing for cognitive radio networks"
    - "active sequential hypothesis testing"
    - "controlled sensing for multihypothesis testing"
    - "eigenvalue based spectrum sensing algorithms for cognitive radio"

Connected through hyperedge HE-4 (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"
    - "asymptotically optimal anomaly detection via sequential testing"
    - "active sequential hypothesis testing"
    - "controlled sensing for multihypothesis testing"

Connected through hyperedge HE-5 (induced by source paper "nonparametric composite hypothesis testing in an asymptotic regime"):
  with members:
    - "rate distortion bounds on bayes risk in supervised learning"
    - "discrimination on the grassmann manifold fundamental limits of subspace classifiers"

Connected through hyperedge HE-6 (induced by source paper "active hypothesis testing on a tree anomaly detection under hierarchical observations"):
  with members:
    - "improved target acquisition rates with feedback codes"
    - "converse bounds for noisy group testing with arbitrary measurement matrices"
    - "optimal nested test plan for combinatorial quantitative group testing"
    - "semiquantitative group testing"
    - "quickest search over multiple sequences with mixed observations"
    - "active sequential hypothesis testing"
    - "controlled sensing for multihypothesis testing"
    - "non adaptive group testing explicit bounds and novel algorithms"

Connected through hyperedge HE-7 (induced by source paper "learning to detect an oddball target"):
  with members:
    - "neural dissimilarity indices that predict oddball detection in behaviour"
    - "asymptotically optimal anomaly detection via sequential testing"
    - "universal outlier hypothesis testing"
    - "active sequential hypothesis testing"
    - "controlled sensing for multihypothesis testing"

Connected through hyperedge HE-8 (induced by source paper "neural dissimilarity indices that predict oddball detection in behaviour"):
  with members:
    - "active search with a cost for switching actions"
    - "active sequential hypothesis testing"
    - "controlled sensing for multihypothesis testing"
    - "randomized sensor selection in sequential hypothesis 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)