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: "exact learning of juntas from membership queries"
  Abstract: In this paper, we study adaptive and non-adaptive exact learning of Juntas from membership queries. We use new techniques to find new bounds, narrow some of the gaps between the lower bounds and upper bounds and find new deterministic and randomized algorithms with small query and time complexities. #R##N#Some of the bounds are tight in the sense that finding better ones either gives a breakthrough result in some long-standing combinatorial open problem or needs a new technique that is beyond the existing ones.

Connected through hyperedge HE-1 (induced by source paper "group testing an information theory perspective"):
  with members:
    - "quantitative group testing in the sublinear regime"
    - "understanding phase transitions via mutual information and mmse"
    - "performance of group testing algorithms with near constant tests per item"
    - "learning erd h o s r enyi random graphs via edge detecting queries"
    - "information theoretic and algorithmic thresholds for group testing"
    - "novel impossibility results for group testing"
    - "noisy adaptive group testing bounds and algorithms"
    - "near optimal noisy group testing via separate decoding of items"

Connected through hyperedge HE-2 (induced by source paper "almost optimal testers for concise representations"):
  with members:
    - "almost optimal distribution free junta testing"
    - "distribution free junta testing"
    - "near log convexity of measured heat in discrete time and consequences"
    - "exact learning from an honest teacher that answers membership queries"
    - "beyond talagrand functions new lower bounds for testing monotonicity and unateness"
    - "settling the query complexity of non adaptive junta testing"
    - "testing unateness of real valued functions"
    - "an widetilde o n queries adaptive tester for unateness"

Connected through hyperedge HE-3 (induced by source paper "almost optimal distribution free junta testing"):
  with members:
    - "near log convexity of measured heat in discrete time and consequences"
    - "beyond talagrand functions new lower bounds for testing monotonicity and unateness"
    - "settling the query complexity of non adaptive junta testing"
    - "testing unateness of real valued functions"
    - "a polynomial lower bound for testing monotonicity"
    - "tight bounds for the distribution free testing of monotone conjunctions"
    - "new algorithms and lower bounds for monotonicity testing"
    - "a o n monotonicity tester for boolean functions over the hypercube"

Connected through hyperedge HE-4 (induced by source paper "adaptive exact learning of decision trees from membership queries"):
  with members:
    - "learning software constraints via installation attempts"
    - "exact learning from an honest teacher that answers membership queries"
    - "learning disjunctions of predicates"
    - "linear time constructions of some d restriction problems"
    - "on exact learning monotone dnf from membership queries"

Connected through hyperedge HE-5 (induced by source paper "exact learning from an honest teacher that answers membership queries"):
  with members:
    - "almost optimal cover free families"
    - "progress on polynomial identity testing ii"
    - "linear time constructions of some d restriction problems"
    - "on exact learning monotone dnf from membership queries"
    - "learning boolean halfspaces with small weights from membership queries"
    - "bounds on the rate of disjunctive codes"
    - "efficient computation of representative sets with applications in parameterized and exact algorithms"
    - "analytical approach to parallel repetition"

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.LG(Machine Learning)