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: "representing objects relations and sequences"
  Abstract: Vector Symbolic Architectures (VSAs) are high-dimensional vector representations of objects (eg., words, image parts), relations (eg., sentence structures), and sequences for use with machine learning algorithms. They consist of a vector addition operator for representing a collection of unordered objects, a Binding operator for associating groups of objects, and a methodology for encoding complex structures. #R##N#We first develop Constraints that machine learning imposes upon VSAs: for example, similar structures must be represented by similar vectors. The constraints suggest that current VSAs should represent phrases ("The smart Brazilian girl") by binding sums of terms, in addition to simply binding the terms directly. #R##N#We show that matrix multiplication can be used as the binding operator for a VSA, and that matrix elements can be chosen at random. A consequence for living systems is that binding is mathematically possible without the need to specify, in advance, precise neuron-to-neuron connection properties for large numbers of synapses. #R##N#A VSA that incorporates these ideas, MBAT (Matrix Binding of Additive Terms), is described that satisfies all Constraints. #R##N#With respect to machine learning, for some types of problems appropriate VSA representations permit us to prove learnability, rather than relying on simulations. We also propose dividing machine (and neural) learning and representation into three Stages, with differing roles for learning in each stage. #R##N#For neural modeling, we give "representational reasons" for nervous systems to have many recurrent connections, as well as for the importance of phrases in language processing. #R##N#Sizing simulations and analyses suggest that VSAs in general, and MBAT in particular, are ready for real-world applications.

Connected through hyperedge HE-1 (induced by source paper "density encoding enables resource efficient randomly connected neural networks"):
  with members:
    - "a theory of sequence indexing and working memory in recurrent neural networks"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "the high dimensional geometry of binary neural networks"
    - "bitwise neural networks"
    - "ternary neural networks for resource efficient ai applications"
    - "quantized neural networks training neural networks with low precision weights and activations"
    - "provably bounded optimal agents"

Connected through hyperedge HE-2 (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"
    - "short term memory capacity in networks via the restricted isometry property"
    - "quantum computation via sparse distributed representation"
    - "vector symbolic architectures answer jackendoff s challenges for cognitive neuroscience"

Connected through hyperedge HE-3 (induced by source paper "hyperdimensional computing nanosystem"):
  with members:
    - "hardware optimizations of dense binary hyperdimensional computing rematerialization of hypervectors binarized bundling and combinational associative memory"
    - "rapid co optimization of processing and circuit design to overcome carbon nanotube variations"

Connected through hyperedge HE-4 (induced by source paper "neural distributed autoassociative memories a survey"):
  with members:
    - "autoscaling bloom filter controlling trade off between true and false positives"
    - "dense associative memory is robust to adversarial inputs"
    - "fast similarity sketching"
    - "optimal las vegas locality sensitive data structures"
    - "associative memories to accelerate approximate nearest neighbor search"
    - "dense associative memory for pattern recognition"
    - "set similarity search beyond minhash"
    - "distance sensitive bloom filters without false negatives"

Connected through hyperedge HE-5 (induced by source paper "integer echo state networks hyperdimensional reservoir computing"):
  with members:
    - "reservoir computing approaches for representation and classification of multivariate time series"
    - "a theory of sequence indexing and working memory in recurrent neural networks"
    - "reservoir computing and extreme learning machines using pairs of cellular automata rules"
    - "deep reservoir computing using cellular automata"
    - "quantized neural networks training neural networks with low precision weights and activations"
    - "vector symbolic architectures answer jackendoff s challenges for cognitive neuroscience"

Connected through hyperedge HE-6 (induced by source paper "theory of the superposition principle for randomized connectionist representations in neural networks"):
  with members:
    - "associative long short term memory"
    - "neural turing machines"
    - "quantum computation via sparse distributed representation"
    - "vector symbolic architectures answer jackendoff s challenges for cognitive neuroscience"

Connected through hyperedge HE-7 (induced by source paper "connectionist symbolic machine intelligence using cellular automata based reservoir hyperdimensional computing"):
  with members:
    - "classification of occluded objects using fast recurrent processing"
    - "unifying visual semantic embeddings with multimodal neural language models"
    - "controlling recurrent neural networks by conceptors"
    - "distributed representations of words and phrases and their compositionality"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "modeling the mind a brief review"):
  with members:
    - "neural turing machines"
    - "natural language processing almost from scratch"

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)