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: "convolutional long short term memory networks for recognizing first person interactions"
  Abstract: In this paper, we present a novel deep learning based approach for addressing the problem of interaction recognition from a first person perspective. The proposed approach uses a pair of convolutional neural networks, whose parameters are shared, for extracting frame level features from successive frames of the video. The frame level features are then aggregated using a convolutional long short-term memory. The hidden state of the convolutional long short-term memory, after all the input video frames are processed, is used for classification in to the respective categories. The two branches of the convolutional neural network perform feature encoding on a short time interval whereas the convolutional long short term memory encodes the changes on a longer temporal duration. In our network the spatio-temporal structure of the input is preserved till the very final processing stage. Experimental results show that our method outperforms the state of the art on most recent first person interactions datasets that involve complex ego-motion. In particular, on UTKinect-FirstPerson it competes with methods that use depth image and skeletal joints information along with RGB images, while it surpasses all previous methods that use only RGB images by more than 20% in recognition accuracy.

Connected through hyperedge HE-1 (induced by source paper "three stream fusion network for first person interaction recognition"):
  with members:
    - "robust and discriminative labeling for multi label active learning based on maximum correntropy criterion"
    - "spatiotemporal pyramid network for video action recognition"
    - "non linear temporal subspace representations for activity recognition"
    - "learning and refining of privileged information based rnns for action recognition from depth sequences"
    - "boosted multiple kernel learning for first person activity recognition"
    - "attentional pooling for action recognition"
    - "action recognition with coarse to fine deep feature integration and asynchronous fusion"
    - "going deeper into first person activity recognition"

Connected through hyperedge HE-2 (induced by source paper "a correlation based feature representation for first person activity recognition"):
  with members:
    - "learning latent sub events in activity videos using temporal attention filters"
    - "temporal segment networks towards good practices for deep action recognition"
    - "spatiotemporal residual networks for video action recognition"
    - "deep residual learning for image recognition"
    - "draw a recurrent neural network for image generation"
    - "the evolution of first person vision methods a survey"
    - "action recognition with trajectory pooled deep convolutional descriptors"
    - "analyzing the performance of multilayer neural networks for object recognition"

Connected through hyperedge HE-3 (induced by source paper "deep dual relation modeling for egocentric interaction recognition"):
  with members:
    - "a correlation based feature representation for first person activity recognition"
    - "unsupervised learning of depth and ego motion from video"
    - "learning robot activities from first person human videos using convolutional future regression"
    - "two stream collaborative learning with spatial temporal attention for video classification"
    - "learning to poke by poking experiential learning of intuitive physics"
    - "going deeper into first person activity recognition"
    - "temporal segment networks towards good practices for deep action recognition"
    - "spatial transformer networks"

Connected through hyperedge HE-4 (induced by source paper "multi grained spatio temporal modeling for lip reading"):
  with members:
    - "adding attentiveness to the neurons in recurrent neural networks"
    - "end to end audiovisual speech recognition"
    - "lrw 1000 a naturally distributed large scale benchmark for lip reading in the wild"
    - "lipnet end to end sentence level lipreading"
    - "combining residual networks with lstms for lipreading"
    - "lip reading sentences in the wild"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "two stream convolutional networks for action recognition in videos"

Connected through hyperedge HE-5 (induced by source paper "top down attention recurrent vlad encoding for action recognition in videos"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "deep visual attention prediction"
    - "adding attentiveness to the neurons in recurrent neural networks"
    - "attention is all we need nailing down object centric attention for egocentric activity recognition"
    - "lsta long short term attention for egocentric action recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "learning to detect violent videos using convolutional long short term memory"

Connected through hyperedge HE-6 (induced by source paper "lsta long short term attention for egocentric action recognition"):
  with members:
    - "making third person techniques recognize first person actions in egocentric videos"
    - "scaling egocentric vision the epic kitchens dataset"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "adding attentiveness to the neurons in recurrent neural networks"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"
    - "attention is all we need nailing down object centric attention for egocentric activity recognition"
    - "top down attention recurrent vlad encoding for action recognition in videos"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-7 (induced by source paper "multi modal egocentric activity recognition using audio visual features"):
  with members:
    - "boosted multiple kernel learning for first person activity recognition"
    - "compact cnn for indexing egocentric videos"
    - "the evolution of first person vision methods a survey"

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.CV(Computer Vision and Pattern Recognition)