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: "a general non probabilistic theory of inductive reasoning"
  Abstract: Probability theory, epistemically interpreted, provides an excellent, if not the best available account of inductive reasoning. This is so because there are general and definite rules for the change of subjective probabilities through information or experience; induction and belief change are one and same topic, after all. The most basic of these rules is simply to conditionalize with respect to the information received; and there are similar and more general rules. 1 Hence, a fundamental reason for the epistemological success of probability theory is that there at all exists a well-behaved concept of conditional probability. Still, people have, and have reasons for, various concerns over probability theory. One of these is my starting point: Intuitively, we have the notion of plain belief; we believe propositions2 to be true (or to be false or neither). Probability theory, however, offers no formal counterpart to this notion. Believing A is not the same as having probability 1 for A, because probability 1 is incorrigible3; but plain belief is clearly corrigible. And believing A is not the same as giving A a probability larger than some 1 - c, because believing A and believing B is usually taken to be equivalent to believing A & B.4 Thus, it seems that the formal representation of plain belief has to take a non-probabilistic route. Indeed, representing plain belief seems easy enough: simply represent an epistemic state by the set of all propositions believed true in it or, since I make the common assumption that plain belief is deductively closed, by the conjunction of all propositions believed true in it. But this does not yet provide a theory of induction, i.e. an answer to the question how epistemic states so represented are changed tbrough information or experience. There is a convincing partial answer: if the new information is compatible with the old epistemic state, then the new epistemic state is simply represented by the conjunction of the new information and the old beliefs. This answer is partial because it does not cover the quite common case where the new information is incompatible with the old beliefs. It is, however, important to complete the answer and to cover this case, too; otherwise, we would not represent plain belief as conigible. The crucial problem is that there is no good completion. When epistemic states are represented simply by the conjunction of all propositions believed true in it, the answer cannot be completed; and though there is a lot of fruitful work, no other representation of epistemic states has been proposed, as far as I know, which provides a complete solution to this problem. In this paper, I want to suggest such a solution. In [4], I have more fully argued that this is the only solution, if certain plausible desiderata are to be satisfied. Here, in section 2, I will be content with formally defining and intuitively explaining my proposal. I will compare my proposal with probability theory in section 3. It will turn out that the theory I am proposing is structurally homomorphic to probability theory in important respects and that it is thus equally easily implementable, but moreover computationally simpler. Section 4 contains a very brief comparison with various kinds of logics, in particular conditional logic, with Shackle's functions of potential surprise and related theories, and with the Dempster - Shafer theory of belief functions.

Connected through hyperedge HE-1 (induced by source paper "factored probabilistic belief tracking"):
  with members:
    - "stochastic simulation algorithms for dynamic probabilistic networks"
    - "tractable inference for complex stochastic processes"
    - "context specific independence in bayesian networks"
    - "the factored frontier algorithm for approximate inference in dbns"
    - "rao blackwellised particle filtering for dynamic bayesian networks"
    - "on the relation between kappa calculus and probabilistic reasoning"
    - "a simple insight into iterative belief propagation s success"

Connected through hyperedge HE-2 (induced by source paper "defaults and infinitesimals defeasible inference by nonarchimedean entropy maximization"):
  with members:
    - "general belief measures"
    - "the probability of a possibility adding uncertainty to default rules"
    - "what does a conditional knowledge base entail"
    - "another perspective on default reasoning"
    - "nonmonotonic reasoning preferential models and cumulative logics"

Connected through hyperedge HE-3 (induced by source paper "a qualitative linear utility theory for spohn s theory of epistemic beliefs"):
  with members:
    - "arguing for decisions a qualitative model of decision making"
    - "an order of magnitude calculus"
    - "from conditional oughts to qualitative decision theory"
    - "on the relation between kappa calculus and probabilistic reasoning"

Connected through hyperedge HE-4 (induced by source paper "on the relation between kappa calculus and probabilistic reasoning"):
  with members:
    - "parallel belief revision"
    - "non monotonic reasoning and the reversibility of belief change"
    - "reasoning with qualitative probabilities can be tractable"

Connected through hyperedge HE-5 (induced by source paper "arguing for decisions a qualitative model of decision making"):
  with members:
    - "an order of magnitude calculus"
    - "from conditional oughts to qualitative decision theory"

Connected through hyperedge HE-6 (induced by source paper "an order of magnitude calculus"):
  with members:
    - "on reasoning in networks with qualitative uncertainty"
    - "from conditional oughts to qualitative decision theory"

Connected through hyperedge HE-7 (induced by source paper "general belief measures"):
  with members:
    - "reasoning with qualitative probabilities can be tractable"
    - "the probability of a possibility adding uncertainty to default rules"

Connected through hyperedge HE-8 (induced by source paper "on transformations between probability and spohnian disbelief functions"):
  with members:
    - "an experimental comparison of numerical and qualitative probabilistic reasoning"

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.AI(Artificial Intelligence)