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: "on exact learning monotone dnf from membership queries"
  Abstract: In this paper, we study the problem of learning a monotone DNF with at most $s$ terms of size (number of variables in each term) at most $r$ ($s$ term $r$-MDNF) from membership queries. This problem is equivalent to the problem of learning a general hypergraph using hyperedge-detecting queries, a problem motivated by applications arising in chemical reactions and genome sequencing. #R##N#We first present new lower bounds for this problem and then present deterministic and randomized adaptive algorithms with query complexities that are almost optimal. All the algorithms we present in this paper run in time linear in the query complexity and the number of variables $n$. In addition, all of the algorithms we present in this paper are asymptotically tight for fixed $r$ and/or $s$.

Connected through hyperedge HE-1 (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"
    - "exact learning of juntas from membership queries"
    - "linear time constructions of some d restriction problems"

Connected through hyperedge HE-2 (induced by source paper "on learning graphs with edge detecting queries"):
  with members:
    - "noise resilient group testing limitations and constructions"

Connected through hyperedge HE-3 (induced by source paper "exact learning from an honest teacher that answers membership queries"):
  with members:
    - "exact learning of juntas from membership queries"
    - "almost optimal cover free families"
    - "progress on polynomial identity testing ii"
    - "linear time constructions of some d restriction problems"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "exact learning of juntas from membership queries"):
  with members:
    - "derandomizing chernoff bound with union bound with an application to k wise independent sets"
    - "linear time constructions of some d restriction problems"

Connected through hyperedge HE-5 (induced by source paper "optimal learning of specifications from examples"):
  with members:
    - "using dynamic analysis to generate disjunctive invariants"
    - "learning semantic string transformations from examples"

Connected through hyperedge HE-6 (induced by source paper "non adaptive learning a hidden hipergraph"):
  with members:
    - "almost optimal cover free families"
    - "linear time constructions of some d restriction problems"

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)