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: "learning without recall a case for log linear learning"
  Abstract: We analyze a model of learning and belief formation in networks in which agents follow Bayes rule yet they do not recall their history of past observations and cannot reason about how other agents' beliefs are formed. They do so by making rational inferences about their observations which include a sequence of independent and identically distributed private signals as well as the beliefs of their neighboring agents at each time. Fully rational agents would successively apply Bayes rule to the entire history of observations. This leads to forebodingly complex inferences due to lack of knowledge about the global network structure that causes those observations. To address these complexities, we consider a Learning without Recall model, which in addition to providing a tractable framework for analyzing the behavior of rational agents in social networks, can also provide a behavioral foundation for the variety of non-Bayesian update rules in the literature. We present the implications of various choices for time-varying priors of such agents and how this choice affects learning and its rate.

Connected through hyperedge HE-1 (induced by source paper "naive bayesian learning in social networks"):
  with members:
    - "bayesian heuristics for group decisions"

Connected through hyperedge HE-2 (induced by source paper "defending non bayesian learning against adversarial attacks"):
  with members:
    - "finite time analysis of the distributed detection problem"
    - "reaching approximate byzantine consensus with multi hop communication"
    - "byzantine vector consensus in complete graphs"
    - "iterative byzantine vector consensus in incomplete graphs"
    - "exponentially fast parameter estimation in networks using distributed dual averaging"
    - "distributed detection over noisy networks large deviations analysis"
    - "consensus of multi agent networks in the presence of adversaries using only local information"
    - "large deviations performance of consensus innovations distributed detection with non gaussian observations"

Connected through hyperedge HE-3 (induced by source paper "bayesian heuristics for group decisions"):
  with members:
    - "learning without recall by random walks on directed graphs"
    - "groupthink and the failure of information aggregation in large groups"
    - "exponentially fast parameter estimation in networks using distributed dual averaging"

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.SI(Social and Information Networks)