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 active information storage in input driven systems"
  Abstract: Information theory and the framework of information dynamics have been used to provide tools to characterise complex systems. In particular, we are interested in quantifying information storage, information modification and information transfer as characteristic elements of computation. Although these quantities are defined for autonomous dynamical systems, information dynamics can also help to get a "wholistic" understanding of input-driven systems such as neural networks. In this case, we do not distinguish between the system itself, and the effects the input has to the system. This may be desired in some cases, but it will change the questions we are able to answer, and is consequently an important consideration, for example, for biological systems which perform non-trivial computations and also retain a short-term memory of past inputs. Many other real world systems like cortical networks are also heavily input-driven, and application of tools designed for autonomous dynamic systems may not necessarily lead to intuitively interpretable results. #R##N#The aim of our work is to extend the measurements used in the information dynamics framework for input-driven systems. Using the proposed input-corrected information storage we hope to better quantify system behaviour, which will be important for heavily input-driven systems like artificial neural networks to abstract from specific benchmarks, or for brain networks, where intervention is difficult, individual components cannot be tested in isolation or with arbitrary input data.

Connected through hyperedge HE-1 (induced by source paper "jidt an information theoretic toolkit for studying the dynamics of complex systems"):
  with members:
    - "assessing coupling dynamics from an ensemble of time series"
    - "efficient transfer entropy analysis of non stationary neural time series"
    - "towards a synergy based approach to measuring information modification"
    - "shared information new insights and problems in decomposing information in complex systems"
    - "information theoretic measures of influence based on content dynamics"
    - "nonnegative decomposition of multivariate information"

Connected through hyperedge HE-2 (induced by source paper "guided self organization of input driven recurrent neural networks"):
  with members:
    - "advances in optimizing recurrent networks"

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)