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: "quantum computation via sparse distributed representation"
  Abstract: Quantum superposition states that any physical system simultaneously exists in all of its possible states, the number of which is exponential in the number of entities composing the system. The strength of presence of each possible state in the superposition—i.e., the probability with which it would be observed if measured—is represented by its probability amplitude coefficient.  The assumption that these coefficients must be represented physically disjointly from each other, i.e., localistically, is nearly universal in the quantum theory/computing literature. Alternatively, these coefficients can be represented using sparse distributed representations (SDR), wherein each coefficient is represented by a small subset of an overall population of representational units and the subsets can overlap.  Specifically, I consider an SDR model in which the overall population consists of Q clusters, each having K binary units, so that each coefficient is represented by a set of Q units, one per cluster. Thus, K Q  coefficients can be represented with KQ units. We can then consider the particular world state, X, whose coefficient’s representation, R(X), is the set of Q units active at time t to have the maximal probability and the probabilities of all other states, Y, to correspond to the size of the intersection of R(Y) and R(X). Thus, R(X) simultaneously serves both as the representation of the particular state, X, and as a probability distribution over all states. Thus, set intersection may be used to classically implement quantum superposition. If algorithms exist for which the time it takes to store (learn) new representations and to find the closest-matching stored representation (probabilistic inference) remains constant as additional representations are stored, this would meet the criterion of quantum computing.  Such algorithms, based on SDR, have already been described. They achieve this "quantum speed-up" with no new esoteric technology, and in fact, on a single-processor, classical (Von Neumann) computer.#R##N##R##N#NeuroQuantology | June 2012 | Volume 10 | Issue 2 | Page 311-315

Connected through hyperedge HE-1 (induced by source paper "a theory of sequence indexing and working memory in recurrent neural networks"):
  with members:
    - "integer echo state networks hyperdimensional reservoir computing"
    - "associative long short term memory"
    - "openxbow introducing the passau open source crossmodal bag of words toolkit"
    - "distributed sequence memory of multidimensional inputs in recurrent networks"
    - "representing objects relations and sequences"
    - "short term memory capacity in networks via the restricted isometry property"
    - "vector symbolic architectures answer jackendoff s challenges for cognitive neuroscience"

Connected through hyperedge HE-2 (induced by source paper "theory of the superposition principle for randomized connectionist representations in neural networks"):
  with members:
    - "associative long short term memory"
    - "representing objects relations and sequences"
    - "neural turing machines"
    - "vector symbolic architectures answer jackendoff s challenges for cognitive neuroscience"

Connected through hyperedge HE-3 (induced by source paper "transparallel mind classical computing with quantum power"):
  with members:
    - "geometric analogue of holographic reduced representation"

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.NE(Neural and Evolutionary Computing)